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This article provides a systematic review of research in the development of reliable and autonomous robotic systems capable not only of data collection but also of independent data analysis and interpretation. To better understand how to achieve such functions, four core modules are discussed. First, a data acquisition and preprocessing pipeline ensures the quality, consistency, and usability of incoming data by using multiple sensors to collect data and Manhattan distance to conduct correlation analysis. Second, using Probabilistic Neuro-Fuzzy Systems integrated with Artificial Intelligence (AI) along with Temporal Fusion Net and the model based on the SE-ResNet50 network, they are constructed and optimized for real-time diagnosis models. Third, fault prediction models including a cyber-physical system and a hybrid model forecast failures and maximize accuracy. Fourth, human-computer interaction can be improved by applying cloud-assisted wearable devices that are significant for reducing the interaction challenges and helping in real-time monitoring and diagnosis. In addition to the proposed framework, the paper analyzes key challenges according to the methods. It also discusses potential solutions and future development strategies. The findings of this study are expected to offer a solid foundation for advancing innovative research that supports the growth and wider adoption of medical robotic diagnostics.