Aging is traditionally characterized as an irreversible biological process driven by the cumulative accrual of molecular damage. In this article, I propose and articulate a fundamentally distinct conceptual framework: aging as a dynamical, information-driven process that can be modeled, predicted, and actively steered using artificial intelligence (AI). I position AI not as a passive analytical tool but as an enabling core technology capable of transforming aging into a controllable biological trajectory. By integrating multi-omic and multi-scale biological data, AI enables the precise optimization of the timing, sequencing, and personalization of therapeutic interventions, thereby reframing longevity science as a problem of temporal systems control. This perspective establishes a new theoretical foundation for precision geromedicine and next-generation longevity biotechnology. Keywords: Artificial Intelligence in Aging,Aging as a Dynamical System,Closed-Loop Control Systems,Predictive Gerontology,Biological Aging Clocks,Precision Geromedicine,Longevity Biotechnology,Temporal Systems Control,AI-Driven Interventions,Multi-Scale Modeling,Personalized Aging Trajectories,Senotherapeutics,Cellular Reprogramming,Healthspan Extension,Systems Biology of Aging