The vitrification of high-level nuclear waste within borosilicate glass and its disposition within a multi-barrier repository deep underground is accepted as the best form of disposal. Here, the ability of machine learning to predict both static and dynamic glass leaching behavior is analysed using large-scale unstructured multi-source data, covering a diverse range of experimental conditions and glass compositions. Machine learning can accurately predict leaching behavior, predict missing data, and time forecast. Accuracy depends upon the type of learning algorithm, model input variables, and diversity or size of the underlying dataset. For static leaching, the bagged random forest method predicts well, even when either pH or glass composition are neglected as input variables, additionally showing potential in predicting independent glass dissolution data. For dynamic leaching, accuracy improves if replacing final pH with a species dissolution rate as an input variable, although results show no preferred output species (Si, Na, or Al).