Biological systems exhibit complex, nonlinear dynamics that are commonly studied using mechanistic models based on differential equations. While these models provide principled insight into biological processes, their simulation and analysis become computationally expensive and labor intensive as system complexity grows, limiting scalable analysis of various dynamical regimes. Machine learning offers promising opportunities to accelerate computation, but many data-driven approaches ignore mechanistic knowledge and generalize poorly across conditions, limiting their utility in biomedical and clinical applications. This talk presents a perspective that leverages AI surrogate representations for biological dynamical systems. By integrating biological structure into learning, these approaches enable scalable and uncertainty-aware prediction of biological dynamics, supporting systematic exploration and reliable inference in biomedical research. The talk will conclude with an Alzheimer’s disease case study illustrating disease progression and longitudinal clinical trajectories.