Nonadiabatic coupling (NAC) plays a central role in driving nonadiabatic dynamics in various photophysical and photochemical processes. However, the high computational cost of NAC limits the time scale and system size of quantum dynamics simulation. By developing a machine learning (ML) framework and applying it to a traditional CH3N3PbI3 perovskite, we demonstrate that the various ML algorithms (XGBoost, LightGBM, and random forest) combined with three descriptors (sine matrix, MBTR, and SOAP) can predict accurate NACs that all agree well with the direct calculations, particularly for the combination of LightGBM and sine matrix descriptor showing the best performance with a high correlation coefficient of ≤0.87. The simulated nonradiative electron-hole recombination time scales agree well with each other between the NACs obtained from direct calculations and ML prediction. The study shows the advantage in accelerating quantum dynamics simulations using ML algorithms.