Reservoir operations significantly impact river systems, making it important to accurately represent this human dimension in large-scale hydrological and water resource models. However, this critical human influence has long been underrepresented due to the complex nature of reservoir management and the lack of operation records. In this study, we developed MODROM (MOdular Data-driven Reservoir Operation Model), which simplifies reservoir operations into basic operation modules and their seasonal changes. We calibrated this model using extensive operation data for over 400 large reservoirs across the United States and developed a machine learning model to predict operations for reservoirs lacking historical records. Our results demonstrate that MODROM performs well when calibrated with comprehensive operation data, though accuracy decreased for larger reservoirs with more complex regulation capabilities. By testing the model’s predictive power on a subset of reservoirs held out as data-scarce, our approach demonstrated strong potential, though the limited size of our dataset introduced some uncertainty. Compared to existing approaches, MODROM provides a significant advantage in predicting operations for data-scarce reservoirs, making it potentially useful for hydrological and water resource modeling at large scale.