This study presents a novel approach to calculating Al chemical shifts in the zeolite MFI using a combination of ML-derived interaction potentials (MLPs) to comprehensively sample the configuration space and the kernel ridge regression (KRR), alongside other simpler regression methods, to predict the Al chemical shifts for the MLP-sampled structures. The accuracy and reliability of these methods are validated with respect to the reference chemical shift values obtained from density functional theory (DFT) calculations. The vast ML-based acceleration of the simulations enables a comprehensive investigation of various factors affecting the chemical shift, including water content, temperature, and aluminium pairing. The predicted chemical shifts, evaluated at experimentally relevant conditions, are compared both to previous computational studies and the existing experimental NMR spectra, allowing us to propose an alternative assignment of T-sites to experimentally observed Al resonances. Also, this study, to the best of our knowledge, is the first to comprehensively investigate Al NMR chemical shifts under operando conditions in such a complex zeolite as MFI.