Glacier evolution models based on temperature-index approaches are commonly used to assess hydrological impacts of glacier changes. However, in large-scale applications, these models lack calibration frameworks that efficiently leverage sparse high-resolution observations, limiting their ability to resolve seasonal mass changes. Machine learning approaches can potentially address this limitation by learning relationships from sparse data that are transferable in space and time, including to unmonitored glaciers. Here, we present the Mass Balance Machine (MBM), a data-driven mass balance model based on the XGBoost architecture, designed to provide accurate and high spatio-temporal resolution regional-scale reconstructions of glacier mass balance. We trained and tested MBM using a dataset of approximately 4000 seasonal and annual point mass balance measurements from 32 glaciers across heterogeneous climate settings in mainland Norway, spanning from 1962 to 2021. To assess the advantage MBM's generalisation capabilities, we compared its predictions on independent test glaciers at various spatio-temporal scales with those of regional-scale simulations from three glacier evolution models. MBM successfully predicted annual and seasonal point mass balance on the test glaciers (RMSE of 0.59–1.00 m w.e. and bias of -0.01–0.04 m w.e.). On seasonal mass balance, MBM outperformed the other models across spatial scales, reducing RMSE by up to 46 % and 25 % on glacier-wide winter and summer mass balance, respectively. Our results demonstrate the capability of machine learning models to generalise across glaciers and climatic settings from relatively sparse mass balance data, highlighting their potential for a wide range of applications.