AI and ML are revolutionizing personalized medicine by facilitating predictive, adaptive, and mechanistic treatment planning. Conventional approaches of therapy are, however, rarely tailored on the patient’s molecular and cellular individuality (as well as systemic variability), with suboptimal clinical efficacy and increased toxicity. AI and ML algorithms exploit high-dimensional data—such as genomics, transcriptomics, proteomics, metabolomics, imaging and longitudinal clinical records to discover predictive biomarkers , to optimize the selection of therapy and to deliver interventions in real time. In oncology they are being applied to understand tumour heterogeneity, predict resistance to therapy and develop immunotherapeutic approaches. In gene and cell therapy, ML algorithms drive optimal CAR-T cell production, gRNA selection in CRISPR based therapies, predict cellular persistence and efficacy. It applies in auto-immune, metabolic and cardiovascular diseases for dynamic dosing and monitoring. Challenges consist of data harmonization, model interpretability, applications in clinical workflow, and regulatory adherence. We outline future directions that include multi-modal data fusion, federated learning, explainable AI and reach toward beyond therapeutic modalities. The convergence of AI and ML with molecular medicine has the unprecedented ability to significantly increase precision, effectiveness and safety in advanced therapy applications, providing a paradigm shift toward truly personalized care