This chapter examines the transformative integration of artificial intelligence into clinical neuroscience practice. The discussion spans the entire care continuum from diagnosis to long-term management. This chapter systematically explores real-world AI applications across neuroimaging analysis, EEG interpretation, clinical decision support, and predictive modeling for disease progression and treatment outcomes. A detailed examination of AI-optimized transcranial direct current stimulation demonstrates how machine learning can personalize neuromodulation therapy. This includes optimizing electrode placement, stimulation parameters, and timing based on individual brain characteristics. The discussion of personalized treatment approaches illustrates AI’s capacity to move beyond standardized protocols toward individualized care. This transformation occurs through treatment selection optimization, patient subtyping, and the integration of digital biomarkers from wearable devices and smartphone applications. This chapter provides extensive coverage of early detection systems for Alzheimer’s disease, showing how AI analysis of multimodal neuroimaging biomarkers can identify subtle brain changes years before clinical symptom onset, potentially expanding therapeutic windows. Significant attention is devoted to implementation challenges. These include data quality and standardization issues, evolving regulatory frameworks, explainability concerns, workflow integration difficulties, and the need for appropriate clinical validation. Throughout, this chapter emphasizes the complementary relationship between AI systems and clinical expertise, positioning AI as augmenting rather than replacing human judgment. The analysis concludes that while AI holds tremendous promise for enhancing precision, accessibility, and effectiveness of neurological care, realizing this potential requires systematic attention to technical, regulatory, ethical, and organizational challenges through collaborative efforts among diverse stakeholders.