In summary, via a combination of machine learning potentialdriven dynamics to sample relevant configuration space and statistical models to predict Al chemical shifts based on the structures sampled, we managed to model a complex zeolitic system (H-MFI) under experimentally relevant conditions, taking into account the effects of temperature, water solvation and specific Al location within the framework. Our results align well with the relevant experimental data and are capable of explaining some of the apparent disagreements (e.g., due to Al pairing in the experimental samples). In addition, we are able to predict how temperature and water loading affects the Al chemical shifts as well as provide mechanistic-level explanations/interpretations. Hence, this combined approach provides an important case study on how highly efficient machine learning algorithms can be coupled to offer predictive accuracy and deeper insights into the structural properties of extremely important industrial catalysts, the aluminosilicate zeolites, under realistic conditions.