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Breast cancer remains one of the most pressing global health concerns, and early detection plays a crucial role in improving survival rates. Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities. However, existing methodologies face persistent challenges, including low image contrast, noise interference, and inaccuracies in segmenting regions of interest. To address these limitations, this study introduces a novel computational framework for analyzing mammographic images, evaluated using the Mammographic Image Analysis Society (MIAS) dataset comprising 322 samples. The proposed methodology follows a structured three-stage approach. Initially, mammographic scans are classified using the Breast Imaging Reporting and Data System (BI-RADS), ensuring systematic and standardized image analysis. Next, the pectoral muscle, which can interfere with accurate segmentation, is effectively removed to refine the region of interest (ROI). The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis (ICA) to enhance contrast, suppress noise, and improve image clarity. Following these enhancements, a robust segmentation technique is employed to delineated abnormal regions. Experimental results validate the efficiency of the proposed framework, demonstrating a significant improvement in the Effective Measure of Enhancement (EME) and a 3 dB increase in Peak Signal-to-Noise Ratio (PSNR), indicating superior image quality. The model also achieves an accuracy of approximately 97%, surpassing contemporary techniques evaluated on the MIAS dataset. Furthermore, its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications. This study presents an advanced and dependable computational framework for mammographic image analysis, effectively addressing critical challenges in noise reduction, contrast enhancement, and segmentation precision. The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic (CAD) systems, with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.