<p>Molecular dynamics simulations provide a microscopic description of atomic motion and have become a central tool for studying chemical and physical processes. Their broader application, however, remains limited by three fundamental challenges. These include the need for accurate interaction potentials, the difficulty of sampling rare events efficiently, and the challenge of extracting mechanistic information from large trajectory data sets. Recent advances in machine learning offer new strategies to address these limitations by learning computationally expensive operators directly from data while incorporating physical constraints such as conservation laws and symmetry. This review examines how machine learning assisted molecular dynamics is advancing two research areas that share a common mathematical framework but operate at very different physical scales namely reactive molecular chemistry and astrophysical systems. In reactive chemical environments, machine learning models learn surrogate force fields capable of describing bond breaking and bond formation, enabling efficient prediction of reaction pathways, species evolution, and free energy landscapes during processes including aggregation, oxidation, dissociation, and transport. Similar concepts are increasingly applied in astrophysical modeling where machine learning approximates complex physical operators that are otherwise computationally prohibitive, including mappings in nonadiabatic dynamics, turbulence closures in simulations of core collapse supernovae, and efficient representations of cosmological and spectroscopic fields. Across these domains the central principle is the approximation of physical operators while preserving the structure of the governing equations. By integrating quantum trained force fields, enhanced sampling guided by learned coordinates, and data driven analysis of trajectories, machine learning augmented molecular dynamics provides a unified computational framework capable of extending simulations across wide spatial and temporal scales. This perspective highlights how the integration of machine learning with molecular dynamics bridges precision and scalability, linking molecular processes such as polycyclic aromatic hydrocarbon chemistry and soot formation with large scale astrophysical phenomena governing the evolution of stars and galaxies.</p>