Interpreting solid-state NMR spectra can be challenging due to signal broadening and overlap. Theoretical calculations of NMR parameters, including those derived from first-principles methods, can provide valuable insights. However, the computational demands of these methods, especially for the large unit cells typical of zeolites, often require adoption of various simplifications, such as disregarding water molecules and/or chargecompensating cations in the models, or representing the system using a single structure (putative global minimum) forgoing the dynamical temperature effects. Machine learning (ML) methods offer a promising alternative to first-principles methods by significantly accelerating either the structure sampling via machine learning potentials or the property predictions. In particular, various ML methods have been tested and adopted for the prediction of NMR-related properties with regression techniques such as least absolute shrinkage and selection operator regression, kernel ridge regression, and Gaussian process regression proved effective in accurately predicting chemical shielding values. Similarly, algorithms based on neural networks have been successfully applied to determine chemical shieldings. Recently, both regression and neural network methods have also been used to predict the shielding tensors and the electric field gradient (EFG) tensor components for quadrupolar nuclei. These ML techniques significantly improve the efficiency of NMR modelling, enabling adoption of more realistic models, while retaining the first-principle method accuracy.