Exploring the Intersection of Machine Learning and Big Data: A Survey

Abstract

The integration of machine learning (ML) with big data has revolutionized industries by enabling the extraction of valuable insights from vast and complex datasets. This convergence has fueled advancements in various fields, leading to the development of sophisticated models capable of addressing complicated problems. However, the application of ML in big data environments presents significant challenges, including issues related to scalability, data quality, model interpretability, privacy, and the handling of diverse and high-velocity data. This survey provides a comprehensive overview of the current state of ML applications in big data, systematically identifying the key challenges and recent advancements in the field. By critically analyzing existing methodologies, this paper highlights the gaps in current research and proposes future directions for the development of scalable, interpretable, and privacy-preserving ML techniques. Additionally, this survey addresses the ethical and societal implications of ML in big data, emphasizing the need for responsible and equitable approaches to harnessing these technologies. The insights presented in this paper aim to guide future research and contribute to the ongoing discourse on the responsible integration of ML and big data.

1. Introduction

The exponential growth of data generated by digital devices, social media platforms, sensors, and the Internet of Things (IoT) has ushered in the era of big data, profoundly transforming various industries and scientific fields. This deluge of data, often referred to as “big data”, is characterized by its immense volume, diversity, high velocity, and uncertain veracity, posing significant challenges for traditional data processing and analysis methods. As organizations and researchers strive to harness the potential of these data, ML has emerged as a crucial tool, offering the ability to extract meaningful insights, make predictions, and drive decision-making processes in ways that were previously unimaginable [1,2].

ML, a subset of artificial intelligence (AI), involves the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed to perform specific tasks. Combining ML with big data has led to groundbreaking applications across various domains, including healthcare, finance, transportation, and beyond. For instance, ML models are now capable of diagnosing diseases from medical images, detecting fraudulent transactions in financial systems, optimizing traffic flows in smart cities, and even predicting customer behavior in retail environments [3,4,5].

However, the convergence of big data and ML is not without challenges. The sheer scale and complexity of big data necessitate new approaches to data management, algorithm design, and computational infrastructure. Traditional ML algorithms, which were not designed to handle the vast amounts of data generated today, often struggle with the issues of scalability, efficiency, and accuracy. Additionally, the heterogeneous nature of big data—encompassing structured data from databases, unstructured data from text and images, and semi-structured data such as JavaScript object notation (JSON) files—further complicates the task of developing effective ML models [6,7,8].

Moreover, integrating ML into big data environments raises critical concerns about model interpretability, data privacy, and ethical considerations. As ML models become more complex, understanding how they arrive at their decisions becomes increasingly difficult, which can be problematic in high-stakes applications such as healthcare and finance. Furthermore, the use of personal and sensitive data in ML models raises significant privacy and security issues, necessitating the development of new techniques to protect individual rights while still enabling the use of large-scale data for analysis [9,10,11,12].

The present survey differentiates itself from the existing literature summarized in Table 1 by providing a more integrative and forward-looking perspective on the intersection of ML and big data. Unlike the surveys in Table 1, which primarily focus on domain-specific applications (e.g., energy, healthcare, and transportation) or challenges (e.g., scalability and interpretability), this survey adopts a broader framework. It systematically identifies critical challenges such as scalability, interpretability, data privacy, and ethical considerations while addressing their interconnections with ML techniques and frameworks.

A key differentiation is its focus on the ethical and societal implications of ML in big data, an area often overlooked in the reviewed surveys. Furthermore, this survey uniquely emphasizes the need for sustainable and privacy-preserving ML, proposing advanced solutions like federated learning and energy-efficient models. By consolidating insights across diverse applications and challenges, it highlights emerging research gaps, such as the integration of ML with legacy systems, the environmental impact of ML operations, and the scalability of algorithms for multi-modal data. This comprehensive perspective not only addresses existing gaps but also charts a roadmap for future research in responsible and scalable ML in big data contexts. In conclusion, this survey makes several key contributions to the understanding and advancement of ML in the context of big data:

• It provides an in-depth exploration of the diverse applications of ML in big data across multiple domains.

• It systematically identifies and analyzes the critical challenges that arise from the application of ML to big data.

• It reviews recent advancements in ML techniques and frameworks that have been developed to address the challenges posed by big data.

• It outlines key areas for future research, focusing on the continued development of scalable, interpretable, and privacy-preserving ML techniques.

• Recognizing the broader impact of ML and big data on society, the survey addresses the ethical challenges associated with bias, fairness, accountability, and the environmental impact of these technologies.

To provide a clear roadmap for the topics explored in this study, Figure 1 offers a visual overview of the key themes and their interconnections. This figure reflects the structure of the study and serves as a guide for the subsequent literature review, ensuring a comprehensive and cohesive exploration of ML applications and challenges in big data. More specifically, the remainder of this paper is structured as follows. Section 2 focuses on ML applications in big data. Section 3 explores the challenges associated with the use of big data with ML. Moreover, Section 4 notes recent advancements and future directions. Section 5 provides a discussion of the subject under consideration. Finally, Section 6 summarizes the findings of this survey.

2. Machine Learning Applications in Big Data

ML has emerged as a powerful tool for unlocking the vast potential of big data, enabling the extraction of meaningful insights from complex and voluminous datasets. Across various industries, ML applications drive significant advancements by leveraging big data to solve problems that were previously unsolvable. This section explores the diverse ways in which ML is applied to big data, transforming fields such as healthcare, finance, transportation, and beyond.

2.1. Healthcare

ML techniques, especially DL, reinforcement learning (RL), and ensemble methods, have demonstrated transformative potential in the healthcare sector, leveraging big data to revolutionize patient care, diagnostics, and treatment planning. One of the most prominent applications is predictive analytics. ML models forecast disease progression, patient outcomes, and potential complications by analyzing large datasets, including patient history, genetic information, and lifestyle factors. These models enable personalized medicine, allowing healthcare providers to tailor treatments to individual patients based on their unique data profiles [20,21,22,23].

Furthermore, ML is extensively used in medical imaging, where convolutional neural networks (CNNs) and other DL techniques have achieved state-of-the-art performance in detecting and diagnosing conditions from images such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans. For instance, ML models can detect early signs of diseases like cancer, often more accurately than human radiologists, by training on vast datasets of labeled medical images. ML algorithms accelerate drug discovery by analyzing genomic data, chemical structures, and protein–ligand interactions to predict viable drug candidates efficiently. These techniques also enable virtual screening, molecular optimization, and drug repurposing, significantly reducing time and cost [24,25,26,27].

Additionally, natural language processing (NLP) techniques are employed to extract valuable insights from unstructured data such as clinical notes, research papers, and patient-reported outcomes. This enables the development of more comprehensive and context-aware healthcare models. Exploiting electronic health records (EHRs) with ML models facilitates real-time monitoring and decision support, enhancing the ability of clinicians to make informed decisions quickly. ML algorithms process diverse EHR data, including medical histories, imaging results, and lab reports, to predict patient outcomes, recommend treatments, and identify potential complications. For instance, NLP can extract critical information from clinical notes, while DL techniques analyze imaging data for timely diagnoses. By enabling real-time decision support, these applications not only improve patient outcomes but also reduce costs and enhance the overall efficiency of healthcare delivery [28,29,30,31].

ML-driven predictive analytics empowers healthcare providers to move from reactive to proactive care by forecasting disease risks, identifying complications early, and tailoring treatments to individual patients. This transformative capability not only enhances patient outcomes but also reduces operational costs, making ML a cornerstone of modern healthcare strategies [32,33,34].

2.2. Finance

In the finance industry, ML is transforming how financial institutions manage risks, detect fraud, and optimize investment strategies. The ability to analyze large volumes of transactional data, market trends, and customer behavior allows ML models to identify patterns and anomalies that may indicate fraudulent activities. While traditional data mining algorithms are proficient in analyzing structured datasets to detect fraud through rule-based pattern matching and historical data analysis, ML offers a transformative edge by handling both structured and unstructured data at scale. For instance, ML models can process transactional records alongside unstructured sources like customer complaints or social media signals to dynamically adapt to new fraud schemes [35,36,37].

Unlike static rule-based data-mining systems, ML models can generalize across scenarios, enabling real-time detection of anomalies in high-frequency financial transactions. An example is the deployment of DL techniques in credit card fraud detection, where models analyze transaction attributes (e.g., time, location, and amount) alongside behavioral patterns, thereby reducing false positives and improving detection accuracy. Building on these capabilities, ML-based fraud detection systems excel at analyzing millions of transactions in real time, identifying suspicious activities with high precision while minimizing false positives. Through continuous learning from newly observed data, these systems evolve to detect emerging fraud patterns and adapt to the ever-changing landscape of financial crime [38,39,40,41].

Risk management is another critical application where ML models assess credit, market risk, and operational risk by analyzing diverse datasets, including historical market data, financial statements, and economic indicators. These models help financial institutions predict potential losses, optimize capital allocation, and comply with regulatory requirements. In the realm of algorithmic trading, ML algorithms process vast amounts of financial data to predict stock prices, execute trades, and manage portfolios, often at speeds and accuracies unattainable by human traders. These models can adapt to changing market conditions in real time, making them invaluable in high-frequency trading environments [42,43,44,45].

Moreover, ML is increasingly used in customer relationship management (CRM) within the finance sector. By analyzing customer data, ML models can predict customer needs, personalize financial products, and optimize marketing strategies. These applications enhance customer satisfaction and retention by providing tailored financial services and improving the overall customer experience [46,47,48,49].

2.3. Transportation and Smart Cities

ML applications in transportation are reshaping urban mobility and the development of smart cities. Autonomous vehicles, one of the most significant advancements in this domain, rely on ML algorithms to process data from a multitude of sensors, including cameras, light detection and ranging (LiDAR), and global positioning systems (GPSs). These algorithms enable vehicles to perceive their environment, make real-time decisions, and navigate complex traffic conditions safely and efficiently. The continuous influx of data from millions of miles driven allows these models to learn and improve, reducing the likelihood of accidents and enhancing the reliability of self-driving cars [50,51,52,53].

In addition to autonomous driving, ML is applied to optimize traffic management systems in smart cities. By analyzing data from traffic sensors, surveillance cameras, and social media, ML models can predict traffic patterns, optimize traffic light sequences, and reduce congestion. This leads to more efficient use of infrastructure, lower emissions, and improved quality of life for urban residents [54,55,56,57].

ML is also crucial in the logistics and supply chain sectors, where it is used to optimize routing, inventory management, and demand forecasting. Companies use ML to predict delivery times, optimize delivery routes, and manage inventory levels in real time, reducing costs and improving customer satisfaction. The integration of ML with IoT devices in smart cities further enhances these capabilities by providing real-time data on traffic, weather conditions, and other factors that influence logistics operations [58,59,60,61].

2.4. Retail and E-Commerce

In the retail and e-commerce industries, ML plays a pivotal role in enhancing customer experience, optimizing operations, and driving sales. One of the most visible applications is in personalized recommendations, where ML algorithms analyze customer data, including browsing history, purchase patterns, and demographic information to recommend products tailored to individual preferences. Big companies have successfully implemented recommendation systems that significantly boost customer engagement and sales [62,63,64,65].

Predictive analytics is another critical application in retail, where ML models forecast demand, optimize pricing strategies, and manage inventory. By analyzing historical sales data, market trends, and external factors such as seasonality and economic conditions, these models can predict which products are likely to be in demand and adjust pricing accordingly to maximize revenue. ML also enables dynamic pricing, where prices are adjusted in real time based on factors such as competitor pricing, customer behavior, and inventory levels [66,67,68,69].

Besides personalization and predictive analytics, ML is used in supply chain management to optimize operations from production to delivery. By analyzing data from various sources, including suppliers, logistics providers, and point-of-sale systems, ML models can optimize inventory levels, reduce lead times, and minimize stockouts. This leads to more efficient operations, lower costs, and better customer service [70,71,72,73].

2.5. Energy and Utilities

The energy and utilities sector is increasingly leveraging ML to optimize operations, improve energy efficiency, and integrate renewable energy sources. One of the primary applications is in predictive maintenance, where ML models analyze data from sensors installed in power plants, wind turbines, and other infrastructure to predict equipment failures before they occur. This enables utilities to perform maintenance proactively, reducing downtime and maintenance costs [74,75,76,77,78].

Coupled with predictive maintenance, ML is applied to optimize energy consumption and improve grid stability. Smart grids, which use ML to analyze data from sensors and meters across the grid, can predict energy demand, optimize energy distribution, and integrate renewable energy sources more effectively. This leads to more efficient use of resources, lower energy costs, and reduced environmental impact [79,80,81,82].

ML also plays a crucial role in the integration of renewable energy sources, such as solar and wind, into the energy grid. By analyzing weather data, ML models can predict the availability of renewable energy, allowing utilities to optimize the use of renewable resources and reduce reliance on fossil fuels. This contributes to the development of a more sustainable and resilient energy system [83,84,85].

2.6. Agriculture and Environmental Monitoring

In agriculture, ML is driving the development of precision farming techniques that increase crop yields, reduce waste, and optimize resource use. By analyzing data from satellites, drones, and IoT devices, ML models can monitor crop health, predict yields, and optimize irrigation and fertilization schedules. These techniques enable farmers to make data-driven decisions that improve productivity and sustainability [86,87,88,89].

ML is also used in environmental monitoring to analyze data from sensors, satellites, and other sources to monitor air and water quality, predict natural disasters, and assess the impact of climate change. For example, ML models can analyze satellite images to detect deforestation, monitor changes in land use, and predict the impact of these changes on biodiversity and ecosystems. These applications are critical for managing natural resources and mitigating the effects of climate change [90,91,92,93].

In precision agriculture, ML algorithms analyze data from various sources, including soil sensors, weather stations, and drones, to optimize planting, irrigation, and harvesting schedules. This results in more efficient use of water, fertilizers, and pesticides, leading to higher crop yields and reduced environmental impact. Additionally, ML is used to develop disease and pest prediction models, enabling farmers to take preventive measures before outbreaks occur [94,95,96,97].

In environmental monitoring, ML is applied to analyze data from satellite imagery, weather models, and ground sensors to track changes in ecosystems, predict natural disasters, and monitor climate change. These models can detect patterns and trends that are not apparent to human observers, providing valuable insights for environmental management and policy making [98,99,100,101].

2.7. Telecommunications

The telecommunications industry has embraced ML to enhance network performance, improve customer experience, and optimize operations. One of the primary applications is in network optimization, where ML models analyze data from network sensors, user devices, and traffic patterns to optimize network performance, reduce latency, and improve service quality. These models can predict network congestion, optimize routing, and allocate resources dynamically based on real-time data [102,103,104,105].

Alongside network optimization, ML is used in CRM to analyze customer data, predict churn, and personalize marketing campaigns. By analyzing customer behavior, usage patterns, and feedback, ML models can identify customers at risk of leaving and develop targeted retention strategies. This leads to improved customer satisfaction and loyalty [106,107,108,109].

ML is also applied in fraud detection, where models analyze call data records, transaction data, and usage patterns to detect fraudulent activities such as subscriber identity module (SIM) card cloning, account takeovers, and identity theft. These models continuously learn from new data, improving their ability to detect and prevent fraud in real time [110,111,112,113].

2.8. Manufacturing and Industry 4.0

In manufacturing, ML is a key enabler of Industry 4.0, driving advancements in automation, predictive maintenance, and quality control. By analyzing data from sensors, machines, and production lines, ML models can predict equipment failures, optimize production schedules, and improve product quality. These applications lead to increased efficiency, reduced downtime, and lower production costs [114,115,116].

Predictive maintenance is one of the most widely adopted applications of ML in manufacturing. By analyzing data from sensors installed in machinery, ML models can predict when equipment is likely to fail and schedule maintenance before a breakdown occurs. This approach reduces unplanned downtime, extends equipment life, and lowers maintenance costs [117,118,119].

In quality control, ML models analyze data from production lines, including images, measurements, and sensor readings, to detect defects and ensure that products meet quality standards. These models can identify patterns and anomalies that human inspectors may miss, leading to higher-quality products and reduced waste [120,121,122].

Furthermore, ML is used in supply chain optimization, where models analyze data from suppliers, logistics providers, and customers to optimize inventory levels, reduce lead times, and improve supply chain resilience. This leads to more efficient operations, lower costs, and better customer service [123,124,125].

2.9. Education and E-Learning

In the education sector, ML is driving the development of personalized learning experiences, intelligent tutoring systems, and predictive analytics for student success. By analyzing data from students’ interactions with learning platforms, assessments, and demographic information, ML models can create personalized learning paths that adapt to each student’s strengths, weaknesses, and learning pace [126,127,128].

Intelligent tutoring systems use ML to provide real-time feedback and guidance to students, simulating one-on-one tutoring. These systems analyze student performance, identify areas where students struggle, and provide targeted interventions to help them improve. This leads to more effective learning and better educational outcomes [129,130,131].

Predictive analytics is also used in education to identify students at risk of dropping out or failing courses. By analyzing data such as attendance records, grades, and engagement with learning platforms, ML models can predict which students are likely to need additional support and recommend interventions to improve their chances of success [132,133,134].

ML is also applied in automated grading and assessment, where models analyze student responses to assignments and exams to provide grades and feedback. This approach reduces the workload for teachers, provides faster feedback to students, and enables more frequent assessments [135,136,137].

2.10. Media and Entertainment

In the media and entertainment industry, ML is transforming content recommendation, production, and distribution. Streaming platforms like Netflix and Spotify use ML algorithms to analyze user preferences, viewing habits, and social media activity to recommend personalized content. These recommendation systems drive user engagement, increase viewing time, and enhance customer satisfaction [138,139,140,141].

ML is also used in content creation and production, where models analyze trends, audience preferences, and historical data to predict the success of movies, TV shows, and music. These models help content creators and producers make data-driven decisions about what content to create, how to market it, and when to release it [142,143,144].

In addition to recommendation and content creation, ML is applied in media distribution to optimize the delivery of content across different platforms and devices. By analyzing data on network conditions, user preferences, and device capabilities, ML models can optimize video streaming quality, reduce buffering, and ensure a seamless viewing experience [145,146,147].

Moreover, ML is increasingly used in social media analytics, where models analyze vast amounts of user-generated content, including posts, comments, and likes, to understand trends, sentiment, and audience engagement. These insights help media companies tailor their content, marketing strategies, and user interactions to maximize engagement and revenue [148,149,150].

2.11. Legal and Compliance

In the legal industry, ML is driving advancements in legal research, contract analysis, and compliance monitoring. Legal research platforms use ML algorithms to analyze vast amounts of legal texts, case law, and statutes to provide relevant search results and legal insights. These tools enable lawyers and legal professionals to conduct research more efficiently and make more informed decisions [151,152,153].

Contract analysis is another critical application where ML models analyze contracts, agreements, and legal documents to identify key terms, obligations, and risks. These models can automate the review of large volumes of contracts, reducing the time and cost associated with contract analysis and ensuring compliance with legal requirements [154,155,156].

ML is also applied in compliance monitoring, where models analyze data from financial transactions, communications, and other sources to detect potential compliance violations, such as insider trading, money laundering, and regulatory breaches. These models help organizations comply with regulatory requirements, avoid legal penalties, and protect their reputations [157,158,159].

Alongside legal research and compliance, ML is used in e-discovery, where models analyze large volumes of electronic data, including emails, documents, and social media, to identify relevant information for legal cases. This approach reduces the time and cost associated with document review and improves the accuracy of e-discovery processes [160,161,162].

2.12. Human Resources and Talent Management

In human resources (HR) and talent management, ML is transforming recruitment, employee engagement, and workforce planning. Recruitment platforms use ML algorithms to analyze resumes, job descriptions, and candidate profiles to match the best candidates with job openings. These models can predict candidate success, optimize the hiring process, and reduce the time to hire [163,164,165].

ML is also applied in employee engagement, where models analyze data from employee surveys, performance reviews, and social media to understand employee sentiment, identify factors that drive engagement, and predict turnover. These insights help HR professionals develop strategies to improve employee satisfaction, reduce turnover, and enhance organizational performance [166,167,168].

Workforce planning is another critical application where ML models analyze data on workforce demographics, skills, and performance to predict future workforce needs and optimize talent management strategies. These models enable organizations to plan for succession, identify skills gaps, and develop targeted training programs to ensure that they have the talent needed to meet their business objectives [169,170,171].

Also, ML is increasingly used in performance management, where models analyze data on employee performance, productivity, and feedback to provide personalized recommendations for development and career growth. These models help organizations identify high-potential employees, provide targeted development opportunities, and improve overall workforce performance [172,173,174].

2.13. Aerospace and Defense

In the aerospace and defense sectors, ML is driving advancements in predictive maintenance, mission planning, and autonomous systems. Predictive maintenance is one of the most widely adopted applications, where ML models analyze data from aircraft sensors and maintenance records, as well as operational data, to predict equipment failures and optimize maintenance schedules. This approach reduces downtime, extends equipment life, and improves safety [175,176,177].

Mission planning is another critical application where ML models analyze data from satellite imagery, weather models, and operational data to optimize flight paths, mission objectives, and resource allocation. These models enable more efficient and effective mission planning, improving the success rate of military and aerospace operations [178,179,180].

ML is also applied in the development of autonomous systems, including unmanned aerial vehicles (UAVs), autonomous ships, and robotic systems. These models enable autonomous systems to perceive their environment, make real-time decisions, and execute complex tasks with minimal human intervention. The integration of ML with big data from sensors, cameras, and other sources enhances the capabilities of autonomous systems, enabling them to operate in dynamic and uncertain environments [181,182,183,184].

Moreover, ML is used in cybersecurity within the aerospace and defense sectors, where models analyze network traffic, user behavior, and threat intelligence data to detect and prevent cyberattacks. These models help organizations protect critical infrastructure, secure sensitive data, and ensure the integrity of their operations [185,186,187].

2.14. Government and Public Policy

In government and public policy, ML is being used to improve decision making, optimize resource allocation, and enhance public services. One of the primary applications is in predictive analytics, where ML models analyze data from various sources, including census data, economic indicators, and social media, to predict social, economic, and political trends. These insights help policymakers develop data-driven policies, allocate resources more effectively, and respond to emerging challenges [188,189,190].

ML is also applied in public safety and law enforcement, where models analyze crime data, social media activity, and other sources to predict crime hotspots, identify suspects, and optimize patrol routes. These models enable law enforcement agencies to prevent crime, allocate resources more effectively, and improve public safety [191,192,193].

In addition to predictive analytics and public safety, ML is used in public health to monitor and respond to outbreaks of diseases, such as COVID-19. By analyzing data from health records, social media, and other sources, ML models can predict the spread of diseases, identify at-risk populations, and recommend targeted interventions to control outbreaks [194,195].

ML is also applied in disaster response and emergency management, where models analyze data from satellite imagery, weather models, and social media to predict natural disasters, assess damage, and optimize response efforts. These models enable governments and humanitarian organizations to respond more quickly and effectively to disasters, saving lives and reducing the impact of disasters on communities [196,197].

Overall, the application of ML in government and public policy enhances the ability of governments to serve their citizens, improve public services, and respond to the challenges of the 21st century [198,199].

These diverse applications across industries underscore the extensive impact of ML in big data environments. To provide a clearer overview of the key studies referenced throughout this section, Table 2 categorizes the literature according to specific application domains, offering a structured guide to the wealth of research discussed.

3. Challenges in Big Data for Machine Learning

The integration of ML with big data offers transformative potential across various domains, yet it also presents a unique set of challenges that must be addressed to fully realize its benefits. As the volume, variety, and velocity of data continue to grow exponentially, traditional ML techniques are often inadequate, necessitating the development of new approaches to tackle these complexities. This section explores the critical challenges that arise when applying ML to big data, focusing on scalability, data quality, model interpretability, privacy, and the management of diverse and high-velocity datasets.

3.1. Scalability of Algorithms

One of the most significant challenges in applying ML to big data is the scalability of algorithms. Traditional ML algorithms, many of which were developed when data volumes were orders of magnitude smaller, struggle to scale effectively in the face of today’s massive datasets. As the size of data increases, both in terms of the number of samples and the dimensionality of features, the computational cost and memory requirements of these algorithms grow exponentially. This challenge is particularly pronounced in real-time applications, such as high-frequency trading or autonomous vehicles, where quick decision making is critical [200,201].

Many conventional ML algorithms, such as support vector machines (SVMs) and k-nearest neighbors (k-NN), are inherently limited by their reliance on operations like pairwise comparisons or matrix computations, which become computationally prohibitive as dataset sizes increase. Additionally, these algorithms are typically designed for static and structured datasets, making them ill-suited for the dynamic, heterogeneous, and unstructured nature of big data. For instance, big data environments often include continuous streams of diverse data types, such as text, images, and sensor readings, which require flexible and adaptive approaches [202,203,204,205].

To address these limitations, significant advancements have been made in distributed computing frameworks, such as Apache Hadoop and Apache Spark, which allow data to be processed in parallel across clusters of machines. Techniques like stochastic gradient descent (SGD) and mini-batch processing have also emerged, enabling scalable training of large models while reducing computational overhead. Furthermore, edge computing and federated learning are gaining traction as paradigms for the decentralization of computation, bringing data processing closer to its source and alleviating the bottlenecks of centralized processing [206,207,208].

Despite these advancements, achieving true scalability remains an ongoing challenge. Efficiently partitioning data across nodes, minimizing communication overhead, and ensuring fault tolerance are critical areas of research. As datasets continue to grow, developing algorithms capable of handling the scale, velocity, and variety of big data will remain a focal point of innovation in ML [209,210].

3.2. Data Quality and Preprocessing

Data quality is a critical concern in big data environments, and it directly impacts the performance and reliability of ML models. Big data are often characterized by their “messiness”—datasets can be incomplete, noisy, inconsistent, and riddled with errors. The presence of outliers, missing values, and duplicated data can lead to biased or inaccurate models if not properly addressed. Moreover, in many cases, big data sources are heterogeneous, combining structured data (such as relational databases) with unstructured data (such as text, images, and videos), each requiring different preprocessing techniques [211,212,213,214].

The process of cleaning and preparing big data for ML involves several complex steps, including data integration, transformation, normalization, and feature extraction. Given the scale of big data, these preprocessing steps can be computationally intensive and time-consuming. Automated data cleaning techniques often struggle with the diversity and complexity of real-world datasets, necessitating human intervention to ensure data integrity. Furthermore, data are often generated in real time, requiring continuous preprocessing to maintain data quality. This dynamic nature of big data presents an additional challenge, as models need to be continually retrained to adapt to new data, which may change in structure and distribution over time [215,216,217,218].

3.3. Model Interpretability

Model interpretability is a cornerstone of ML in high-stakes applications like healthcare, finance, and criminal justice, where decisions carry significant consequences. While complex models such as deep neural networks (DNNs) and ensemble methods offer high predictive accuracy, their “black-box” nature often obscures the reasoning behind their decisions. This lack of transparency can erode trust, impede accountability, and lead to ethical concerns when decisions adversely affect individuals or society [219,220].

Interpretability techniques such as SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and Integrated Gradients (IGs) have emerged to address these challenges. These methods reveal how input features contribute to a model’s output, allowing stakeholders to validate and understand predictions. For instance, in healthcare, interpretable models can explain why certain symptoms or tests lead to a diagnosis, enabling doctors to corroborate or challenge the recommendations. In finance, interpretability ensures that loan approvals or fraud detection decisions can be justified to regulators and affected parties [221,222].

High-stakes applications also demand actionable insights to guide decision making. Counterfactual explanations, which suggest minimal changes to inputs required to alter a prediction, provide clear and user-friendly feedback. For example, a counterfactual explanation might indicate that increasing an applicant’s credit score by a specific amount would lead to loan approval, empowering users to take corrective actions. Similarly, Anchors offer localized, rule-based explanations that enhance the clarity and reliability of predictions in domains such as fraud detection [223,224,225].

Moreover, inherently interpretable models, such as generalized additive models (GAMs) with pairwise interactions, offer transparency by design, enabling stakeholders to understand how features influence outcomes without relying on post hoc explanations. Attention mechanisms in DL, particularly in transformers, further enhance interpretability by highlighting specific input elements, such as words or image regions, that drive predictions [226,227].

The importance of interpretability in high-stakes applications extends beyond technical validation; it fosters trust, supports compliance with regulatory requirements, and ensures ethical accountability. As ML systems become more pervasive in critical domains, interpretability remains a crucial factor in balancing model complexity with societal and operational demands [228,229].

3.4. Privacy and Security

The use of ML in big data environments raises significant privacy and security concerns, especially when dealing with sensitive information such as personal health records, financial data, or behavioral data. The integration of ML with big data often requires the collection, storage, and processing of vast amounts of personal information, which can be vulnerable to breaches and misuse. The challenge of ensuring data privacy is exacerbated by the fact that data are often distributed across multiple platforms and jurisdictions, each with its own set of privacy regulations [230,231,232].

Ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe or the California Consumer Privacy Act (CCPA) in the United States, is a complex task. These regulations impose strict requirements on how data can be collected, stored, and used, and they grant individuals certain rights over their personal data, including the right to be forgotten. ML models, particularly those trained on sensitive data, must be designed to comply with these regulations while still delivering accurate and effective results [233,234,235].

Techniques such as differential privacy, which introduces noise to data to obscure individual identities, and federated learning, which allows models to be trained on decentralized data without the need to share raw data across parties, have been developed to address these privacy concerns. However, these techniques also introduce new challenges, such as increased computational complexity and potential trade-offs between privacy and model performance. Additionally, as ML models become more powerful, there is a growing risk of adversarial attacks, where malicious actors manipulate input data to deceive models, further complicating the security landscape [236,237,238].

3.5. Handling Variety and Velocity of Data

Big data are characterized not only by their sheer volume but also by their variety and velocity. Variety refers to the wide range of data types that must be processed, including structured data (e.g., databases), semi-structured data (e.g., XML files), and unstructured data (e.g., text, images, and video). Each of these data types requires a different processing technique, and integrating them into a cohesive analysis pipeline is a significant challenge. For example, combining numerical data from sensors with textual data from social media and visual data from surveillance cameras into a single predictive model requires sophisticated data fusion techniques [239,240].

Recent advancements in DL have introduced specialized architectures for addressing these challenges. Transformers, originally designed for NLP, have demonstrated exceptional versatility in handling unstructured data such as text and images. Models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) use self-attention mechanisms to capture contextual relationships in sequential data, enabling state-of-the-art performance in tasks like machine translation, text summarization, and image captioning. Beyond NLP, transformers have been adapted to handle tabular data and multi-modal datasets, further expanding their applicability to semi-structured data scenarios [241,242,243].

For semi-structured data represented in graph formats, Graph Neural Networks (GNNs) have emerged as a powerful paradigm. GNNs such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) leverage the structural relationships inherent in graph data to propagate and aggregate information across nodes and edges. This makes them particularly effective in domains such as social network analysis, molecular property prediction, and knowledge graph reasoning. For instance, GNNs can model complex relationships between entities in a knowledge graph, enhancing applications like recommendation systems and fraud detection [244,245,246].

The velocity of big data—the speed at which new data are generated and need to be processed—adds another layer of complexity. In many applications, such as real-time analytics, autonomous vehicles, and financial trading, decisions must be made within milliseconds of receiving new data. Traditional batch processing methods are often insufficient for these scenarios, necessitating the development of real-time data processing frameworks that can handle high-throughput data streams with low latency. Tools such as Apache Kafka and Apache Flink have made significant strides in this area. Furthermore, combining these real-time data frameworks with advanced models like transformers and GNNs has shown promise in enabling end-to-end pipelines that process, analyze, and make predictions on diverse datasets at unprecedented speeds. As a result, the integration of transformers and GNNs into big data analytics represents a significant advancement in the field, addressing the challenges of both variety and velocity. These models not only enhance the ability to process unstructured and semi-structured data but also pave the way for the development of more adaptable and robust systems capable of operating in dynamic environments [247,248,249].

3.6. Ethical and Societal Implications

The adoption of ML in big data applications introduces complex ethical challenges, particularly concerning justice, responsibility, and bias. Justice-related concerns arise when models trained on biased datasets reinforce or amplify existing inequities, such as in hiring or credit scoring, where under-represented groups often face systemic disadvantages. Addressing these issues requires fairness-aware algorithms, bias mitigation techniques in preprocessing, and continuous monitoring to ensure equitable performance [250,251,252].

Responsibility and accountability become critical in high-stakes domains like healthcare and finance, where decisions have significant implications for individuals and society. The opacity of many ML models complicates efforts to assign accountability for harmful outcomes. Clear governance frameworks, explainable AI (XAI) tools, and mechanisms for auditing model decisions are essential to maintain trust and transparency [253].

Bias in ML often stems from data imbalances, such as under-representation of certain demographics, leading to poorer performance for marginalized groups. For instance, facial recognition systems have been shown to exhibit higher error rates for minorities. Techniques like reweighting, fairness constraints, and algorithmic debiasing are necessary to reduce discriminatory outcomes and ensure inclusivity [254,255].

Beyond these concerns, ML has broader societal implications, such as the displacement of workers due to automation and the misuse of AI in surveillance or disinformation campaigns. Proactively addressing these issues through collaborative policy making, ethical design principles, and robust regulatory frameworks will ensure that ML systems advance innovation while upholding societal values [256,257].

3.7. Integration with Legacy Systems

The integration of ML with existing legacy systems presents another significant challenge in the application of big data technologies. Many organizations rely on legacy systems that were not designed to handle the scale, complexity, or velocity of modern big data and ML applications. These systems often lack the computational power, data storage capabilities, and flexibility required for advanced analytics, making it difficult to incorporate ML models effectively [258,259,260].

Upgrading or replacing legacy systems is often costly and time-consuming, and it can introduce significant operational risks. Organizations must carefully plan the integration of new technologies with existing infrastructure to ensure compatibility and minimize disruption. This requires not only technical solutions but also strategic planning, including evaluating the cost–benefit of different integration approaches, ensuring data consistency across systems, and managing the transition process [261,262,263].

The combination of ML models with legacy systems can introduce additional challenges related to data interoperability and system compatibility. Different systems may use incompatible data formats, protocols, or standards, making it difficult to integrate data from multiple sources. Ensuring that ML models can seamlessly access and process data from legacy systems requires robust data integration frameworks, middleware solutions, and APIs that can bridge the gap between old and new technologies [264,265,266].

3.8. Environmental Impact

The environmental impact of big data and ML is an emerging challenge that is gaining increasing attention. The energy consumption associated with the training of large ML models, particularly DL models, is substantial. Data centers that power these computations require significant amounts of electricity, often leading to a large carbon footprint. As the demand for more powerful models and larger datasets grows, so does the environmental impact of ML [267,268,269,270].

Efforts to mitigate this impact include developing more energy-efficient algorithms, optimizing data center operations, and using renewable energy sources. Researchers are exploring techniques such as model pruning, quantization, and knowledge distillation to reduce the computational requirements of ML models without sacrificing performance. Additionally, there is a growing movement toward “green AI”, which advocates for the development of sustainable ML practices that prioritize energy efficiency and environmental responsibility [271,272,273].

Organizations and researchers must also consider environmental impact when deciding on the scale and scope of their ML projects. This includes evaluating trade-offs between model complexity and energy consumption, exploring alternative data sources, and considering the broader environmental implications of deploying ML models at scale. By adopting more sustainable practices, the ML community can help reduce the environmental footprint of big data technologies and contribute to global efforts to combat climate change [274,275,276].

As we delve into the challenges posed by the use of ML with big data, it is essential to have a structured understanding of these complexities. Table 3 provides a clear categorization of the various obstacles identified in this section, offering a roadmap to the primary concerns that researchers and practitioners face.

4. Recent Advances and Future Directions

The ML field, in conjunction with big data, has seen rapid advancements that have significantly enhanced its applications across various domains. As the challenges associated with big data continue to evolve, so do the innovations aimed at overcoming these hurdles, paving the way for future breakthroughs and more efficient, scalable solutions.

4.1. Distributed Machine Learning Frameworks

Recent advancements in distributed ML frameworks have significantly enhanced the ability to process and analyze big data efficiently. Frameworks such as Apache Spark, TensorFlow, and PyTorch have been instrumental in scaling ML algorithms across large datasets by leveraging distributed computing resources. These frameworks allow for parallel processing, reducing the time required to train models on vast amounts of data. Innovations in data parallelism, model parallelism, and hybrid parallelism have further improved the scalability of ML models [277,278,279,280,281].

Recent advancements in DNNs have demonstrated that with the right architectures and optimizations, existing models can effectively handle large-scale data challenges. Techniques such as model parallelism and pipeline parallelism have been pivotal in distributing the computational workload across multiple nodes, reducing the training time required for large models. Furthermore, adaptive optimization algorithms like AdamW and RAdam improve convergence rates for high-dimensional data, enabling efficient training, even in resource-constrained environments [282,283,284].

Automated ML (AutoML) frameworks, including neural architecture search (NAS), have also been critical in identifying optimal network architectures tailored for specific datasets. These techniques automatically tune hyperparameters and design architectures that balance complexity and performance, making it possible to deploy DNNs in scenarios requiring low latency and high accuracy [285,286,287].

Moreover, advancements in hardware-aware model design, such as sparse networks and quantization-aware training, have further reduced the computational and memory requirements of DNNs, enabling their deployment in real-time and edge computing environments. These innovations ensure that large-scale DNNs can adapt to the growing demands of big data analytics, providing robust, scalable, and efficient solutions [288,289].

In addition to these frameworks, there has been a surge in the development of specialized hardware, such as graphics processing units (GPUs), tensor processing units (TPUs), and custom application-specific integrated circuits (ASICs), which are optimized for ML tasks. These hardware advancements, coupled with software innovations, have enabled the training of increasingly complex models, such as DNNs with billions of parameters. The integration of these technologies with cloud computing platforms like AWS, Google Cloud, and Azure has democratized access to high-performance computing, allowing researchers and businesses to scale their ML workflows without significant upfront infrastructure investments [290,291,292,293].

Looking forward, the continued evolution of distributed ML frameworks is expected to focus on improving the efficiency and usability of these systems. Areas such as automatic scaling, fault tolerance, and better support for heterogeneous computing environments (including edge devices) are likely to see significant advancements. Additionally, the development of more sophisticated orchestration tools that can seamlessly manage the life cycle of ML models—from data ingestion to deployment—will be crucial in enabling the widespread adoption of big data-driven ML applications [294,295,296,297].

4.2. Model Interpretability Techniques

The challenge of interpreting complex ML models has driven the development of various techniques aimed at enhancing their transparency and trustworthiness. Widely recognized methods such as LIME and SHAP provide post hoc explanations by approximating the contribution of input features to model predictions. These techniques are essential in domains where understanding a model’s decision-making process is critical for regulatory compliance, ethical considerations, and user trust [298,299,300].

Complementing these approaches, IGs address the attribution problem by integrating the gradients of a model’s output with respect to its input features along a path from a baseline input to the actual input. This method ensures attribution scores are faithful to the model while maintaining completeness and sensitivity. IGs are particularly effective for DNNs, providing detailed insights into feature contributions in tasks such as image recognition and NLP. For example, in medical imaging, an IGs can highlight specific regions in an image that influence diagnostic predictions, enabling practitioners to better understand and validate the model’s outputs [301,302,303].

In addition to feature attribution, anchors provide a rule-based approach to local interpretability by generating “if–then” rules that describe the key conditions under which model predictions remain consistent. Unlike general attribution methods, anchors offer highly precise and localized explanations that are both interpretable and generalizable across similar input data. These rules are particularly valuable in high-stakes applications such as fraud detection or medical diagnostics, where decision stability and clarity are paramount [304,305].

Beyond understanding feature contributions, counterfactual explanations focus on identifying the minimal changes to input data required to alter a model’s output. This method provides actionable insights, making it intuitive and user-friendly for non-technical stakeholders. For example, in loan applications, a counterfactual explanation might suggest that increasing an applicant’s income by a specific amount would lead to loan approval. By offering clear, actionable feedback, counterfactual explanations empower users to make informed decisions based on model outcomes and encourage model accountability [306,307].

Another promising area of research is the development of inherently interpretable models, which achieve transparency by design rather than relying on post hoc explanations. Models like GAMs with pairwise interactions offer a clear and intuitive understanding of how input features contribute to predictions. These models decompose complex relationships into additive components, allowing stakeholders to visualize and interpret the influence of individual features and promoting trust in model predictions [308,309].

Attention mechanisms in DL, particularly in architectures like transformers, have further advanced interpretability by revealing how models prioritize specific parts of the input data. Attention maps provide visual insights into what a model “focuses” on during decision making. For instance, in NLP, attention maps can highlight the words or phrases most influential to a prediction, such as identifying keywords in sentiment analysis. Similarly, in computer vision, attention mechanisms pinpoint regions of interest in an image, such as abnormalities in medical scans. In applications like healthcare, these insights are invaluable for validating AI-driven recommendations, while in autonomous systems, they clarify decision-making processes, improving reliability and safety [310,311,312].

Together, these advanced interpretability techniques—IGs, anchors, counterfactual explanations, inherently interpretable models, and attention mechanisms—extend the capabilities of traditional methods like LIME and SHAP. IGs excel in providing gradient-based attributions for complex neural networks, anchors generate precise rule-based insights for localized explanations, counterfactual explanations bridge transparency with actionable recommendations, inherently interpretable models offer built-in transparency by design, and attention mechanisms enhance model trust by revealing input priorities. Each method caters to specific interpretability needs, ensuring that models remain transparent and trustworthy across diverse domains [313,314,315,316].

Future research on model interpretability is likely to explore more sophisticated techniques that balance the trade-off between model complexity and transparency. Integrating interpretability directly into the model training process, rather than relying solely on post hoc explanations, holds great promise. As ML continues to be applied in critical areas such as healthcare, finance, and legal systems, the demand for interpretable and reliable models will drive further innovation and adoption in these fields [317,318,319].

4.3. Privacy-Preserving Machine Learning

With the growing concern over data privacy, especially in light of stringent regulations like the GDPR and CCPA, privacy-preserving ML has become a focal point of research and development. Techniques such as differential privacy, federated learning, and homomorphic encryption have been developed to protect individual privacy while still enabling the use of ML on sensitive data [320,321,322,323].

Federated learning, in particular, has gained significant traction, as it allows models to be trained on decentralized data sources such as smartphones or IoT devices, without the need to transfer raw data to a central server [324]. This approach not only enhances privacy but also reduces the risk of data breaches. For instance, companies like Google have employed federated learning in their mobile devices to improve predictive text algorithms without compromising user privacy. Homomorphic encryption, which allows computations to be performed on encrypted data, is another technique that is gaining attention. Although computationally intensive, advancements in this area are making it more feasible for practical applications [325,326,327,328].

Looking ahead, the future of privacy-preserving ML will likely focus on making these techniques more efficient and scalable. There is also growing interest in developing new cryptographic protocols that combine the strengths of different privacy-preserving techniques to provide robust and flexible solutions. Additionally, as more data move to the edge (e.g., in IoT and mobile devices), there will be a greater emphasis on ensuring that ML models can operate securely and privately in decentralized environments [329,330,331,332].

4.4. Real-Time Data Processing and Multi-Modal Learning

The increasing demand for real-time insights has driven significant advancements in real-time data processing and multi-modal learning. Real-time data processing frameworks, such as Apache Kafka and Apache Flink, have evolved to handle high-throughput data streams with low latency, enabling ML models to make predictions and decisions on the fly. These advancements are critical in applications such as autonomous driving, financial trading, and fraud detection, where delays in processing can lead to significant consequences [333,334,335].

Multi-modal learning, which involves the integration of various data types (e.g., text, images, audio, and video), has also seen considerable progress. Models that effectively combine information from different modalities can make more accurate and robust predictions. For example, in healthcare, multi-modal models can integrate medical images, genetic data, and clinical notes to provide more comprehensive diagnoses and treatment recommendations. In the entertainment industry, multi-modal learning is used to enhance user experiences by integrating data from video, audio, and user interactions to deliver personalized content [336,337,338,339].

The future of real-time data processing and multi-modal learning will likely involve further improvements in the efficiency and scalability of these systems. Additionally, as the amount of available data continues to grow, there will be a greater focus on developing models that can effectively learn from sparse or imbalanced multi-modal datasets. Advances in techniques such as transfer learning, domain adaptation, and meta-learning will play a crucial role in enabling these models to generalize across different domains and data types [340,341,342].

4.5. Ethical and Societal Implications

The ethical and societal implications of ML and big data are becoming increasingly prominent as these technologies become more pervasive. Concerns about bias, fairness, accountability, and the potential for misuse are driving the development of ethical frameworks and guidelines for the responsible use of ML [343,344].

Recent advancements in fairness-aware ML aim to address biases that may arise from training data or algorithmic design. Techniques such as adversarial debiasing, reweighting, and fairness constraints are being incorporated into ML pipelines to ensure that models make equitable decisions across different demographic groups. Additionally, there is growing interest in XAI, which seeks to make ML models more transparent and understandable, thereby enhancing accountability [345,346,347].

Another critical area of focus is the impact of ML on employment and society. As automation and AI technologies become more capable, there is a growing concern about job displacement and the widening of economic inequalities. Policy-makers, industry leaders, and researchers are increasingly collaborating to develop strategies to mitigate these impacts, such as workforce reskilling programs and policies that promote inclusive growth [348,349].

The future of ethical and societal considerations in ML will likely involve the development of more comprehensive frameworks that address not only technical issues but also the broader social, economic, and legal implications of these technologies. There is also a need for greater public engagement and dialogue about the role of ML in society, as well as for the establishment of regulatory bodies that can oversee the development and deployment of these technologies to ensure they are used responsibly [350,351,352].

4.6. Sustainable Machine Learning

As awareness of environmental issues grows, there is increasing emphasis on the sustainability of ML practices. The energy consumption associated with the training of large-scale models, particularly in DL, has prompted researchers to explore ways to reduce the environmental impact of ML. Recent advancements in energy-efficient algorithms, such as model pruning, quantization, and distillation, have shown promise in reducing the computational resources required without sacrificing model performance [353,354,355].

Another area of focus is the development of “green AI” practices that prioritize energy efficiency and sustainability in the design and deployment of ML models. This includes optimizing data center operations, leveraging renewable energy sources, and exploring alternative approaches to model training that are less resource-intensive. Researchers are also looking at ways to measure and report the carbon footprint of ML projects, enabling more informed decisions about the environmental trade-offs of different approaches [356,357,358].

The future of sustainable ML will likely involve a shift toward more environmentally conscious practices across the entire ML life cycle, from data collection and model training to deployment and maintenance. There will also be a growing emphasis on developing models that are not only accurate and efficient but also socially and environmentally responsible. As the field of ML continues to evolve, sustainability will become an increasingly important criterion for evaluating the success of new technologies [359,360,361].

4.7. Integration of Quantum Computing

Quantum computing represents one of the most exciting frontiers for the future of ML, particularly in the context of big data. Quantum computing offers the potential to solve certain types of problems much faster than classical computers, which could revolutionize fields such as cryptography, optimization, and complex simulations. In ML, quantum algorithms like quantum SVMs and quantum neural networks are being explored for their potential to handle high-dimensional data and perform computations that are infeasible for classical algorithms [362,363,364,365].

Recent advancements in quantum ML (QML) have demonstrated the feasibility of implementing basic quantum algorithms on existing quantum hardware. While current quantum computers are still in the early stages of development and are prone to errors, significant progress is being made in improving their stability and scalability. Hybrid quantum–classical algorithms, which combine quantum computing with classical ML methods, are also being developed to take advantage of quantum speedups where possible while relying on classical computation for tasks where quantum advantages are less clear [366,367,368].

Looking forward, integrating quantum computing with big data and ML is likely to become a major area of research. As quantum hardware continues to improve, the development of quantum ML frameworks and algorithms will accelerate, potentially leading to breakthroughs in areas that require the processing of large, complex datasets. The future will likely see a growing synergy between quantum computing and ML, opening up new possibilities in solving some of the most challenging problems in science, engineering, and beyond [369,370,371,372].

4.8. Collaborative and Transfer Learning

The concepts of collaborative and transfer learning are gaining importance in the context of big data, where the ability to leverage knowledge from different domains or collaborators can significantly enhance model performance and efficiency. Transfer learning allows models trained on one task to be adapted to another related task with limited data, reducing the need for extensive training datasets and computational resources. This is particularly useful in domains where labeled data are scarce or expensive to obtain [373,374,375].

Collaborative learning, which includes techniques like federated learning, enables multiple parties to collaboratively train models without sharing their data, preserving privacy and confidentiality. This approach is particularly relevant in industries such as healthcare and finance, where data privacy is paramount. Recent advancements in federated learning have improved the efficiency of these collaborative models, enabling them to scale to larger datasets and more complex tasks [376,377,378,379].

The future of collaborative and transfer learning will likely see further integration of these techniques with big data technologies, enabling more efficient and privacy-preserving ML across different domains. As the amount of generated data continues to grow, the ability to transfer knowledge across tasks and collaborate securely will become increasingly important for the scaling of ML applications and maximization of the value of big data [380,381,382].

The rapid evolution of ML technologies has led to significant advancements that address some of the core challenges discussed earlier. Table 4 highlights these cutting-edge developments, showcasing the innovative solutions paving the way for more effective and scalable ML applications. Finally, Table 5 outlines the most promising directions in the key areas discussed in this section, providing a forward-looking perspective on where the field is headed.

5. Discussion

The findings of this survey underline the transformative potential of ML in addressing the challenges posed by big data across various domains. However, while the paper identifies numerous applications, analysis reveals recurring patterns and broader themes that warrant deeper exploration. This section critically examines and summarizes these findings, identifies gaps in existing methodologies, proposes a unifying framework for ML applications in big data, and highlights considerations for further research and development.

5.1. Synthesis of Findings

The integration of ML with big data has enabled transformative advancements across numerous domains by leveraging the volume, variety, and velocity of modern datasets. ML’s ability to process large-scale and complex data has revolutionized fields like healthcare, finance, and smart cities, enabling innovations such as predictive diagnostics, real-time fraud detection, and optimized urban traffic systems.

Scalable, interpretable, and privacy-preserving ML approaches play a crucial role in addressing the challenges posed by big data. Scalability allows ML models to handle the growing magnitude and complexity of data, facilitating real-time analytics in domains such as finance, where high-frequency trading requires near-instantaneous predictions, and healthcare, where predictive maintenance of medical equipment can save lives. Techniques like distributed computing and parallelism in frameworks such as Apache Spark and TensorFlow have been pivotal in achieving this scalability.

Interpretability enhances trust and accountability in ML models, particularly in high-stakes applications like medical diagnostics and loan approvals. Methods such as SHAP, IGs, and counterfactual explanations enable stakeholders to understand the rationale behind model predictions, ensuring ethical and informed decision making. This transparency is not only essential for regulatory compliance but also in fostering user trust, which is critical in sectors like healthcare and finance.

Privacy-preserving approaches, including federated learning and differential privacy, address the ethical and regulatory concerns associated with sensitive data. For instance, federated learning enables collaborative model training across decentralized data sources without exposing raw data, ensuring confidentiality in environments like healthcare, where patient data security is paramount. These advancements ensure that data-driven insights can be derived without compromising privacy, thereby balancing innovation with ethical responsibility.

Together, scalable, interpretable, and privacy-preserving ML approaches empower organizations to extract actionable insights from big data while adhering to ethical, legal, and technical standards. These advancements highlight the evolving synergy between technological capabilities and societal needs, underscoring ML’s potential to drive impactful and responsible innovation across industries.

Finally, Table 6 presents case studies highlighting the successful application of ML techniques, which are being widely applied across a range of domains, demonstrating their capacity to address real-world challenges with innovative solutions and illustrating their practical utility and impact. These case studies emphasize the adaptability and transformative potential of ML in diverse fields. By connecting theoretical advancements with practical implementations, they provide valuable insights into the growing influence of ML across industries and domains.

5.2. Critical Analysis

The methodologies discussed in this paper, while effective in certain contexts, expose inherent trade-offs that must be critically evaluated. Supervised learning methods dominate structured data analysis but rely heavily on labeled datasets, which are often expensive or impractical to obtain at scale. In contrast, unsupervised learning and RL offer potential solutions for environments with limited or unstructured data but require more computational resources and sophisticated fine tuning.

Moreover, while distributed ML frameworks and cloud computing platforms address the challenge of scalability, they often overlook data privacy concerns. For instance, federated learning and differential privacy techniques have made strides in preserving individual anonymity, yet their computational overhead and reduced model accuracy remain barriers to widespread adoption. These trade-offs highlight the need for hybrid approaches that integrate the strengths of various methodologies while mitigating their limitations.

Additionally, this study reveals gaps in applying ML to domains with stringent real-time requirements. Applications such as autonomous driving and financial trading demand low-latency decision-making, where traditional batch-processing models often fail. The development of real-time learning algorithms that can adapt to dynamic data streams without significant retraining is, thus, an area of critical importance.

5.3. Toward a Generic Framework

To bridge these gaps and provide a structured approach to ML in big data, this paper proposes a generic framework categorizing applications based on three dimensions: data characteristics, learning methodologies, and domain-specific challenges. Data characteristics are divided into structured (e.g., transactional logs), semi-structured (e.g., XML data), and unstructured types (e.g., medical images and textual data). Each category requires distinct preprocessing techniques and model architectures, underscoring the importance of tailored solutions.

Learning methodologies are classified into supervised, unsupervised, and RL methods, each chosen based on the availability of labeled data, the need for predictive versus exploratory insights, and the adaptability of the application. Finally, domain-specific challenges such as scalability, interpretability, and ethical considerations are highlighted as critical components in designing effective ML pipelines.

For instance, in healthcare, unstructured data such as radiological images necessitate advanced DL models with explainable mechanisms to ensure trustworthiness. In contrast, financial applications often rely on semi-structured transactional data that benefit from scalable supervised learning frameworks. A detailed taxonomy aligning data types, methodologies, and domain challenges provides a roadmap for researchers and practitioners to navigate the complexities of ML applications in big data.

5.4. Future Considerations

While recent advancements in ML have addressed many of the challenges posed by big data, significant gaps remain, particularly in the areas of sustainability, fairness, and interdisciplinarity. The environmental impact of training large-scale models continues to grow, prompting a need for energy-efficient algorithms and “green AI” practices. Efforts such as model pruning, quantization, and the use of renewable energy sources in data centers are promising directions but require further refinement to balance performance with resource conservation.

Ethical considerations, including bias, fairness, and accountability, also demand sustained attention. Bias introduced during data collection and model development risks the perpetuation of systemic inequities, particularly in high-stakes domains like criminal justice and credit scoring. Techniques such as adversarial debiasing and fairness-aware ML must be integrated more comprehensively into the development life cycle of ML systems.

Finally, the integration of emerging paradigms, such as quantum ML and multi-modal learning, offers exciting opportunities to tackle the complexities of big data. Quantum computing holds the potential to address high-dimensional problems in cryptography and optimization, while multi-modal learning enables richer insights by combining disparate data sources such as text, audio, and video. However, these fields are still nascent and require collaborative efforts across disciplines to mature into practical solutions.

In conclusion, this discussion highlights the multifaceted challenges and opportunities at the intersection of ML and big data. By synthesizing findings, critically analyzing methodologies, and proposing a generic framework, this study provides a foundation to advance the responsible and effective use of ML in big data environments. Future work should prioritize sustainability, fairness, and interdisciplinary innovation to ensure that these technologies continue to benefit society.

6. Conclusions

The synergy between ML and big data has unlocked new possibilities across a wide range of domains, driving innovation and transforming industries. However, the full potential of this integration is tempered by several significant challenges that must be addressed to ensure the effective and responsible use of these technologies. This survey has provided a detailed exploration of the current landscape of ML applications in big data, highlighting the critical issues of scalability, data quality, model interpretability, privacy, and the handling of diverse and high-velocity data. While recent advancements have made substantial progress in addressing some of these challenges, there remains considerable work to be done.

Future research should focus on developing more scalable algorithms and frameworks that can efficiently process and analyze the ever-growing volumes of data. Additionally, improving model interpretability will be crucial for building trust in ML applications, particularly in high-stakes domains such as healthcare and finance. Privacy-preserving techniques must also be further refined to balance the need for data-driven insights with the protection of individual privacy rights.

Moreover, as the ethical and societal implications of ML and big data become increasingly apparent, it is essential to develop frameworks and guidelines that ensure these technologies are used in ways that are fair, transparent, and aligned with societal values. Addressing the environmental impact of large-scale ML models and promoting sustainable practices will also be key to the long-term viability of these technologies.

In conclusion, the integration of ML and big data presents both profound opportunities and complex challenges. By continuing to innovate and address the obstacles identified in this survey, researchers and practitioners can unlock the full potential of these technologies, driving progress across industries and contributing to the betterment of society as a whole. This survey serves as a comprehensive resource for understanding the current state of the field and as a guide for future research and development efforts at the intersection of ML and big data.

Author Contributions

E.D. and M.T. conceived of the idea, designed and performed the experiments, analyzed the results, drafted the initial manuscript, and revised the final manuscript. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figure and Tables

Figure 1. An overview of surveyed key topics: ML application and big data challenges.

Table 1

Summary of surveys that capture the intersection of ML and big data.

Reference	Description
[13]	It discusses ML techniques for big data, including supervised, unsupervised, and semi-supervised learning, and their applications in fields like healthcare, agriculture, and robotics. It highlights challenges like data imbalance and offers remedies for researchers.
[14]	This survey explores ML methods tailored for big data applications. It addresses features, challenges, and practical uses of algorithms like classification and clustering, emphasizing their scalability and application in domains like healthcare and finance.
[15]	This is a paper providing an overview of ML methods for big data that examines challenges in processing large datasets and highlights promising approaches like distributed and parallel learning. The paper also discusses open research directions.
[16]	This is a survey focusing on big data and ML applications in the electrical power and energy sectors. It reviews challenges, solutions, and opportunities in applying ML for smart grid operations and energy management.
[17]	This paper compiles challenges associated with the use of ML and big data, categorizing them by the dimensions of big data (volume, velocity, variety, and veracity). It reviews emerging techniques and solutions, offering insights into research gaps and future directions.
[18]	This paper provides a detailed literature survey on ML for big data processing, covering techniques like deep learning (DL), representation learning, and their integration with signal processing. It highlights challenges like high dimensionality and discusses potential research trends.
[19]	This survey provides a discussion of opportunities and challenges associated with ML and big data. It introduces a framework that integrates ML phases (preprocessing, learning, and evaluation) with components like user input, the domain, and system architecture.

Table 2

Structured summary of key studies across ML application domains.

Topic	References	Description
Healthcare	[20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]	ML applications in predictive analytics, medical imaging, drug discovery, and healthcare data management.
Finance	[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49]	ML for risk management, fraud detection, algorithmic trading, and CRM.
Transportation & Smart Cities	[50,51,52,53,54,55,56,57,58,59,60,61]	ML applications in autonomous vehicles, traffic management, and smart city infrastructure.
Retail & E-commerce	[62,63,64,65,66,67,68,69,70,71,72,73]	ML for personalized recommendations, predictive analytics, dynamic pricing, and supply chain management.
Energy & Utilities	[74,75,76,77,78,79,80,81,82,83,84,85]	ML applications in predictive maintenance, energy optimization, smart grids, and integration of renewable energy sources.
Agriculture & Environmental Monitoring	[86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101]	ML for precision farming, environmental monitoring, disaster prediction, and resource management.
Telecommunications	[102,103,104,105,106,107,108,109,110,111,112,113]	ML for network optimization, customer retention, fraud detection, and service quality improvement.
Manufacturing & Industry 4.0	[114,115,116,117,118,119,120,121,122,123,124,125]	ML applications in predictive maintenance, quality control, supply chain optimization, and automation in manufacturing.
Education & E-learning	[126,127,128,129,130,131,132,133,134,135,136,137]	ML applications in personalized learning, intelligent tutoring systems, predictive analytics for student success, and automated grading.
Media & Entertainment	[138,139,140,141,142,143,144,145,146,147,148,149,150]	ML for content recommendation, media production, distribution, and social media analytics.
Legal & Compliance	[151,152,153,154,155,156,157,158,159,160,161,162]	ML applications in legal research, contract analysis, compliance monitoring, and e-discovery.
HR & Talent Management	[163,164,165,166,167,168,169,170,171,172,173,174]	ML applications in recruitment, employee engagement, workforce planning, and performance management.
Aerospace & Defense	[175,176,177,178,179,180,181,182,183,184,185,186,187]	ML for predictive maintenance, mission planning, autonomous systems, and cybersecurity in aerospace and defense.
Government & Public Policy	[188,189,190,191,192,193,194,195,196,197,198,199]	ML applications in predictive analytics, public safety, law enforcement, and public health monitoring.

Table 3

Categorization of challenges & key issues in big data for ML.

Topic	References	Description
Scalability of Algorithms	[200,201,202,203,204,205,206,207,208,209,210]	Discusses challenges related to scaling ML algorithms to process massive datasets efficiently. This includes distributed computing and algorithm optimization techniques.
Data Quality and Preprocessing	[211,212,213,214,215,216,217,218]	Focuses on the importance of data quality in big data, including dealing with noise, missing values, and the complexity of preprocessing diverse datasets.
Model Interpretability	[219,220,221,222,223,224,225,226,227,228,229]	Addresses the difficulty in interpreting complex ML models, especially in high-stakes applications where transparency is crucial.
Privacy and Security	[230,231,232,233,234,235,236,237,238]	Explores privacy concerns, data security risks, and challenges related to ensuring compliance with data protection regulations while using big data.
Handling Variety and Velocity of Data	[239,240,241,242,243,244,245,246,247,248,249]	Discusses the challenges associated with the variety (structured, unstructured, and semi-structured) and velocity of big data, requiring real-time processing and integration.
Ethical and Societal Implications	[250,251,252,253,254,255,256,257]	Focuses on the ethical issues surrounding big data and ML, including bias, fairness, accountability, and the broader societal impact.
Integration with Legacy Systems	[258,259,260,261,262,263,264,265,266]	Discusses the challenges of integrating modern ML techniques and big data technologies with existing legacy systems.
Environmental Impact	[267,268,269,270,271,272,273,274,275,276]	Highlights the environmental concerns related to the high energy consumption of ML models and the need for sustainable AI practices.

Table 4

Recent advances: charting the path for ML research and development.

Focus Area	References	Description
Distributed ML Frameworks	[277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293]	Advances in distributed ML frameworks and specialized hardware, such as Apache Spark, TensorFlow, GPUs, TPUs, and custom ASICs, improving the scalability and performance of ML tasks.
Advancing Interpretability	[298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316]	Development of interpretability techniques such as SHAP, LIME, and inherently interpretable models (e.g., GAMs) to enhance the transparency and understanding of ML models.
Privacy-Preserving ML	[320,321,322,323,324,325,326,327,328]	Implementation of privacy-preserving techniques, including differential privacy, federated learning, and homomorphic encryption, to protect data privacy in ML applications.
Real-Time Data Processing	[333,334,335,336,337,338,339]	Enhancements in real-time data processing frameworks like Apache Kafka and Apache Flink and advancements in multi-modal learning integrating text, images, audio, and video for more accurate ML models.
Ethical and Societal Implications	[343,344,345,346,347,348,349]	Introduction of fairness-aware ML techniques and XAI to address ethical concerns, mitigate biases, and ensure the responsible use of ML in high-stakes domains.
Sustainable ML	[353,354,355,356,357,358]	Development of energy-efficient algorithms and sustainable ML practices to reduce the environmental impact of large-scale models, focusing on the creation of green AI.
Quantum Computing in ML	[362,363,364,365,366,367,368]	Early-stage research on quantum computing applications in ML, including quantum SVMs and quantum neural networks, showing potential for solving complex, high-dimensional data problems.
Collaborative and Transfer Learning	[373,374,375,376,377,378,379]	Advances in federated learning and collaborative ML models that enable distributed training across multiple entities without sharing raw data, enhancing privacy, efficiency, and cross-domain knowledge transfer in ML.

Table 5

Future directions in ML research and development.

Research & Development Areas	References	Description
Scaling Distributed ML	[294,295,296,297]	Enhancing the scalability of distributed ML frameworks, with a focus on automatic scaling, fault tolerance, and edge computing support.
Advancing Interpretability	[317,318,319]	Developing more sophisticated techniques to ensure that ML models remain interpretable without compromising on complexity.
Enhancing Privacy Techniques	[329,330,331,332]	Research aimed at making privacy-preserving methods like differential privacy and federated learning more scalable and efficient for broader use.
Optimizing Real-Time Systems	[340,341,342]	Improving the efficiency and scalability of systems that process real-time data, which is crucial for time-sensitive applications in various fields.
Addressing Ethical Challenges	[350,351,352]	Formulating frameworks that address the ethical, social, and legal challenges posed by the increasing use of ML technologies.
Sustainable ML	[359,360,361]	Focusing on reducing the environmental impact of ML, with efforts to develop energy-efficient models and green AI practices.
Quantum Computing Integration	[369,370,371,372]	Pioneering the integration of quantum computing into ML, focusing on stability, scalability, and tackling complex data problems.
Innovations in Collaborative Learning	[380,381,382]	Advancing techniques in federated and transfer learning to improve efficiency, security, and privacy in collaborative ML applications.

Table 6

Case studies demonstrating successful applications of ML methods across various domains.

Domain	Case Study	Methodology	Outcome
Healthcare	Detecting diabetic retinopathy	DL with CNNs	Improved diagnostic accuracy and clinician support
Finance	Fraud detection in high-frequency transactions	SHAP for feature attribution	Enhanced fraud detection and reduced false positives
Transportation and Smart Cities	Traffic congestion prediction in urban areas	GNNs	Reduced travel times and optimized city planning
Retail & E-commerce	Personalized product recommendations	Collaborative filtering in recommender systems	Increased customer engagement and sales
Energy and Utilities	Smart grid energy optimization	GNN for load distribution	Reduced energy waste and optimized resource allocation
Agriculture and Environmental Monitoring	Crop yield prediction using satellite imagery	CNNs for remote sensing	Improved agricultural planning and yield optimization
Telecommunications	Network traffic forecasting	Recurrent neural networks	Improved quality of service and reduced network delays
Manufacturing and Industry 4.0	Predictive maintenance in industrial equipment	Time-series analysis with LSTMs	Reduced downtime and maintenance costs
Education & E-learning	Personalized learning platforms	Adaptive learning algorithms	Improved learning outcomes and user engagement
Media and Entertainment	Content recommendation on streaming platforms	RL	Increased viewer retention and platform engagement
Legal and Compliance	Legal document classification and search	NLP with transformers	Faster case research and improved compliance tracking
HR and Talent Management	Resume screening and job matching	Decision trees and NLP	Accelerated recruitment process and better candidate matching
Aerospace and Defense	Aircraft fault detection using sensor data	Random forests and anomaly detection	Improved safety and reduced maintenance costs
Government and Public Policy	Predicting the spread of infectious diseases	Epidemiological modeling with ML	Informed policy decisions and effective resource allocation

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