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Accurate diagnosis of neurological disorders relies on a thorough examination of brain microstructure, especially white matter. Manually analyzing MRI and DTI scans is frequently both time-consuming and prone to inconsistency. The proposed AI-powered platform automates the analysis of MRI and DTI scans by leveraging Fractional Anisotropy (FA) values alongside image processing and segmentation techniques. The system employs deep learning models, including Convolutional Neural Networks and Random Forest classifiers, to identify abnormalities such as brain tumors, Alzheimer’s disease, and Parkinson’s disease. Furthermore, 3D tractography visualization reveals defective fiber pathways, whereas the chatbot assists users with fundamental clinical information. The results demonstrate that the proposed system provides clear visual outputs, enhances diagnostic accuracy, and reduces radiologists’ workload. This provides scalability and clinical relevance while facilitating early diagnosis and detection.