A novel approach using machine learning algorithms is used to predict high-resolution groundwater-level changes, both current and in the future. The Southern African Development Community (SADC) is dependent on groundwater resources to meet freshwater demand but knowledge of groundwater resources in the region is hampered by the lack of available data, especially at a local scale. Improving the ability to generate near-real-time and high-resolution groundwater data is imperative to support effective groundwater management in the SADC. In this study, an autoregressive approach is applied to develop a set of generalized gradient-boosting decision tree models that can predict 30-day groundwater-level changes, both current and 1 month ahead. The approach integrates numerous readily available satellite-based, land-surface-model, and hydrogeological datasets to train, calibrate and test the gradient-boosting decision tree models. The models achieve a mean absolute error of 15.27 and 12.78 cm, for the groundwater-level-change predictions that are current and 1 month ahead, respectively. The models were used to predict monthly groundwater-level changes, both current and 1 month ahead, at a 10-km resolution, in the Ramotswa/North West/Gauteng dolomite aquifers of Southern Africa. When compared to observed in-situ groundwater-level changes, the mean absolute error for the current and 1-month-ahead groundwater-level changes were 21.23 and 22.26 cm, respectively. While the results are largely satisfactory, challenges associated with limited target data, and extreme values associated with groundwater abstraction, reduce model performance. Nonetheless, the approach demonstrates the applicability of machine learning to generate local groundwater information to improve groundwater management.