Affecting around 3 billion people worldwide, neurological diseases form a significant global health load. Combining data from current developments in diagnosis accuracy, treatment augmentation, and sickness management, this work reviews the revolutionary influence of machine learning (ML) in neurological care. In clinical, neuroimaging, and genetic settings within neurology, we investigate using multiple ML paradigms—supervised, unsupervised, and reinforcement learning. With ML models reaching accuracy levels of up to 96.3% in diseases like Parkinson’s, our study shows significant improvements in the early detection and surveillance of well-known neurological diseases. The chapter evaluates challenges in clinical application, including model interpretability, privacy protection, and data quality assurance. According to present data, future technologies such as federated learning and explainable artificial intelligence (AI) hold promise to solve these problems while preserving high-performance criteria. Especially considering the standardization of validation methods and the integration of multimodal data sources, we draw attention to notable shortcomings in current research. Our findings imply that good use of ML in neurology calls for careful attention to ethical issues, following legal guidelines, and maintenance of human supervision in clinical decision-making. Although ML shows great promise in improving neurological care, future efforts should give top priority to the development of strict validation frameworks, the improvement of data quality, the enhancement of model interpretability, and the strengthening of multidisciplinary cooperation between doctors and ML experts.