Knowledge of glacier ice volumes is crucial for constraining future sea level potential, evaluating freshwater resources, and assessing impacts on societies, from regional to global. Motivated by the disparity in existing ice volume estimates, we present IceBoost, a global Machine Learning framework to model individual glacier ice thickness distributions. IceBoost is a gradient-boosted tree trained with 3.7 million global ice thickness measurements and an array of 34 numerical features. The model’s error aligns within 10% of existing models outside polar regions and is up to 30-40% lower at high latitudes. We find that providing supervision by exposing the model to available glacier thickness measurements reduces the error by up to a factor 2 to 3. A feature ranking analysis reveals that geodetic information are the most informative variables, while ice velocity can improve the model performance by 6% at high latitudes. A major feature of IceBoost is its ability to generalize beyond the training domain, producing meaningful ice thickness distributions across all global regions, including ice sheet peripheries.