Neurological disorders represent a major global health burden, affecting millions of individuals and contributing significantly to disability, morbidity, and mortality. Early and accurate diagnosis remains a critical challenge due to the complexity of neurological diseases, overlapping clinical presentations, and variability in disease progression. Artificial Intelligence (AI), encompassing machine learning, deep learning, computer vision, and natural language processing technologies, has emerged as a transformative tool in neurological diagnostics. AI systems can analyze complex clinical, imaging, electrophysiological, and genomic datasets to identify disease patterns, improve diagnostic accuracy, and support clinical decision-making. Applications have demonstrated promising results in conditions such as Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, stroke, brain tumors, and neurodevelopmental disorders. Despite significant advancements, challenges related to data quality, algorithm transparency, ethical considerations, regulatory approval, and clinical integration remain barriers to widespread adoption. This review examines the current role of AI in neurological disorder diagnosis, evaluates opportunities and limitations, and explores future directions for AI-enabled neurology. Findings suggest that AI-assisted diagnostic systems can significantly enhance neurological care while complementing rather than replacing clinician expertise.