Previously acquired data could be utilised in predicting glass dissolution kinetics at long times, but the application of machine learning methods needs to be assessed. Here, the dissolution processes of two Li-Na borosilicate ‘base glasses’ at 40 and 90 °C were investigated by SEM-EDS, NMR and Raman spectroscopy. Boron and sodium machine learning predictions were excellent when considering other normalised releases as features. However, extrapolating the training feature space yielded poorer performance and the absence of incorporated waste elements resulted in underestimated predicted long-term lithium and silicon releases. Faster dissolution kinetics were observed for MW than MW-½Li but the MW-½Li gel layer at 40 °C trapped more water. Whilst BO₃ rings leached preferentially at 90 °C, surface enrichment of BO₃ at 40 °C suggested [BO₄]¯ transformed prior to dissolution. Results were consistent with interdiffusion being significant at 40 °C and interface-coupled dissolution precipitation beyond 7 days at 90 °C.