Predicting the kinetic pathways of biomolecular systems at all-atom resolution is crucial for understanding protein function and drug efficacy, yet this task is hindered by the immense computational cost of conventional molecular dynamics (MD) simulations. While deep learning has revolutionized static structure prediction and equilibrium ensemble sampling, simulating the kinetics of conformational transitions remains a critical challenge. We introduce BioKinema, a physically grounded generative model that predicts continuous-time, all-atom biomolecular trajectories at a fraction of the cost of traditional simulations. In particular, BioKinema utilizes a scalable diffusion architecture with temporal attention mechanisms derived from Langevin dynamics. It employs a hierarchical forecasting-and-interpolation strategy to overcome the error accumulation that often plagues long-horizon generation. Through extensive validation, we demonstrate that BioKinema generates physically stable and dynamically accurate trajectories suitable for rigorous downstream analysis. The model captures key conformational transitions related to protein function. For protein-ligand complex systems, it successfully elucidates mechanisms such as induced-fit conformational changes and allosteric responses. Furthermore, BioKinema leverages enhanced sampling data to predict rare kinetic events, emerging as a powerful tool for estimating ligand unbinding pathways. Collectively, these results establish BioKinema as a robust alternative to MD that bridges the gap between static structure and dynamic function, enabling high-throughput exploration of the kinetic landscape for structural biology and drug discovery.