Longevity medicine is transforming healthcare by shifting the focus from disease treatment toward the preservation of function, resilience, and healthspan. In parallel, artificial intelligence (AI) has emerged as a powerful catalyst accelerating this transition through the integration and interpretation of multidimensional biological and behavioral data. AI-driven systems can now analyze genomics, epigenomics, proteomics, microbiome signatures, digital biomarkers, lifestyle metrics, and environmental exposures to identify early deviations from healthy aging trajectories before clinical disease manifests. These predictive capabilities enable personalized preventive strategies tailored to an individual’s biological aging profile rather than chronological age alone. AI-supported longevity medicine therefore facilitates precision prevention through adaptive interventions involving nutrition, metabolic optimization, sleep regulation, stress management, continuous biosensing, and targeted therapeutics. Moreover, AI contributes to the evolution of healthcare systems from reactive episodic care toward adaptive and continuously monitored models emphasizing long-term physiological resilience. However, the integration of AI into longevity medicine also raises important scientific, ethical, and societal challenges, including data fragmentation, unequal access to preventive technologies, risks of overmedicalization, and concerns regarding privacy and governance. Bridging siloed biomarker ecosystems through interoperable data infrastructures, federated learning, and digital twin technologies will be essential for clinically meaningful predictive models. Ultimately, AI has the potential to redefine modern preventive medicine by enabling proactive, personalized, and age-resilient healthcare. The future success of AI-enhanced longevity medicine will depend on ensuring that technological innovation remains accurate, ethically grounded, clinically relevant, and equitably accessible across populations.