Estimation of the seismic risk associated with infrastructures requires site-specific seismic hazard studies. Further, for nonlinear time history analysis, one requires broadband ground motion. In modern times, physics-based simulations (PBS) for deriving the ground motion for future earthquakes have been considered. The PBS helps decrease the uncertainties related to hazard estimation compared to ground motion prediction equations. The PBS methods have a specific frequency threshold limit resulting from high computational demand. Hence, hybrid methods are required to attain broadband spectra for the simulated ground motion. This study uses a new artificial neural network (ANN)-based model to generate broadband ground motion spectra using the low-frequency spectral acceleration from PBS, source, path, and site parameters as input variables. A detailed parametric study and performance evaluation was made to identify the optimal input parameters in conjunction with the best-suited ANN architecture. The performance of the ANN model is demonstrated for Iwate (�� 6.9, 2008) earthquake. We found that the predicted values from the developed ANN model agree with the recorded data. Furthermore, time histories are generated using the spectral ordinate matching technique from the estimated broadband spectra.