Enzymes are nature’s catalysts, driving chemical reactions that are essential for life. However, understanding how enzymes function at the molecular level remains challenging because an enzyme can bind its substrate in many different ways and proceed through multiple reaction pathways, many of which are difficult to capture experimentally. As a result, studying enzymatic activity often requires detailed, system-specific knowledge that is either unavailable or difficult to obtain. Even when such information is accessible, modeling these complex processes remains a major challenge. To address these fundamental limitations, I will present a physics-informed machine learning (ML) framework that integrates ML with state-of-the-art statistical physics and quantum-mechanics (QM)–based methods. This approach enables an automated exploration of catalytic mechanisms (1), dynamically identifies key transition states (2), and provides quantitative insight into the thermodynamics and kinetics of the entire catalytic process (1-4). The resulting framework is fast, cost-effective, comprehensive, and transferable, making it a promising platform for further development and applications beyond traditional enzyme catalysis. In this context, I will briefly discuss three promising future directions. The first involves the development of fast and accurate QM potentials to study challenging catalytic reactions involving significant charge transfer and highly polarized transition states, such as metalloenzyme catalysis with allostery, heterogeneous catalysis, and enzymatic plastic degradation. The second direction focuses on developing a dynamic covalent docking platform using explainable AI for high-throughput screening of covalent drug candidates targeting chronic and infectious diseases. The final application concerns modeling entropy-driven assembly processes with broad relevance to the materials and pharmaceutical industries, including polymer aggregation and biomolecular condensation