This chapter examines the transformative convergence of artificial intelligence and neuroscience, tracing how AI methodologies have evolved from early computational models to sophisticated deep learning systems that are revolutionizing brain research and clinical practice. The historical trajectory spans four distinct eras: early computational models (1940s–1960s), the cognitive revolution (1970s–1980s), computational neuroscience (1990s–2000s), and the current deep learning revolution (2010s–present). Contemporary AI applications encompass enhanced neuroimaging analysis, high-throughput neural data processing, brain-computer interfaces, and personalized neurological medicine. A particularly significant development is the paradigm shift from descriptive group-level analyses to predictive individual-patient modeling. This shift enables early detection of neurodegenerative disorders, treatment response prediction, and disease trajectory forecasting. Key AI methodologies transforming neuroimaging include machine learning classification algorithms, deep learning for image segmentation and reconstruction, multimodal integration techniques, graph-based connectivity analysis, and interpretable AI approaches. This chapter illustrates these advances through a detailed case study of early Alzheimer’s disease detection using machine learning. This case study demonstrates how multimodal predictive models can identify preclinical patterns years before symptom onset. This chapter acknowledges challenges in interpretability, generalizability, and clinical implementation. However, it positions this AI-neuroscience convergence as a fundamental transformation. This convergence promises more sophisticated brain analyses across multiple scales and improved outcomes for neurological disorders.