Understanding glacier mass changes is crucial for assessing freshwater availability in many regions of the world. We present the Mass Balance Machine, a machine learning model that learns from sparse measurements of glacier mass change to make predictions on unmonitored glaciers. Using data from Norway, we show that the model provides accurate estimates of mass changes at different spatiotemporal scales. Our findings show that machine learning can be a valuable tool to improve such predictions.