Neurological disorders, including Alzheimer's disease, Parkinson's disease, epilepsy, and multiple sclerosis, affect hundreds of millions of individuals worldwide. Timely and accurate diagnosis remains a critical challenge due to the heterogeneous nature of these conditions and the limited availability of labeled clinical data. This study investigates the application of unsupervised learning techniques — including clustering algorithms, autoencoders, self-organizing maps (SOMs), and graph-based methods — to support the early detection and characterization of neurological disorders. Leveraging multimodal datasets comprising EEG signals, MRI scans, and clinical biomarkers, we demonstrate that unsupervised models can extract latent disease-specific patterns without requiring labeled annotations. Our framework is evaluated on publicly available neuroimaging and electrophysiology datasets, achieving a silhouette score of 0.74 for patient clustering and a reconstruction error reduction of 38% over baseline autoencoders. The results underscore the promise of unsupervised learning as a scalable, cost-effective complement to supervised diagnostic pipelines.