Neurodegenerative diseases pose a significant global health challenge, necessitating precise and early diagnosis for effective management and intervention. This paper presents an in-depth exploration of machine learning and deep learning algorithms applied to the classification and prediction of neurodegenerative diseases, with a particular focus on advancements in diagnostic accuracy. The study reviews current literature, emphasizing segmentation of affected regions in neuroimaging, prediction of ADHD-based diseases, feature selection techniques, multimodal data integration, and the explainability of AI-based models. Methods encompass hand-crafted and automatic feature selection, large dataset transfer learning, and multimodal medical image registration. Performance optimization strategies and explainable models for biomedical mental disorder prediction are discussed. The results showcase promising advancements, demonstrating enhanced disease classification and prediction accuracy. The study discusses potential implications for improved healthcare outcomes and proposes avenues for further research, emphasizing the crucial role of explainable AI and multimodal data integration in medical data processing and disease prediction.