Abstract
<jats:p>This chapter presents an integrative framework — predictive-personalized artificial intelligence (AI) — for digital mental health, combining real-time risk forecasting with dynamically adaptive therapeutic interventions. Unlike existing approaches that separate prediction and intervention, this framework utilizes machine learning, natural language processing, and mobile sensing to integrate behavioral, linguistic, and physiological data to anticipate episodes such as depressive relapses or suicidal ideation. The chapter details closed-loop system architecture linking prediction engines with intervention modules, highlighting personalization, adaptivity, and temporal sensitivity. Ethical and clinical issues including algorithmic bias, data privacy, false positives, and accountability are critically examined. The framework is positioned as a scalable augmentation to human-centered care, advancing a user-focused, anticipatory vision for AI in mental health.</jats:p>