Several alternative methods for calculating chemical shifts have been developed beyond the Lippmaa's approximation. The 2parameter (2p-LASSO) equation ( ) and the 5-parameter (5p-LASSO) equation ( ) were proposed by Lei et al. These models were developed using the least absolute shrinkage and selection operator (LASSO) regression, which is a linear regression technique that adds an L1 regularization penalty, shrinking some coefficients to zero, which simplifies the model by selecting only the most relevant features. These models utilize various descriptors based on bond lengths d and bond angles α to predict chemical shielding with a semi-quantitative accuracy with mean absolute error (MAE) of 1.27 ppm, providing a computationally efficient alternative to ab initio methods while allowing for interpretability of the predictions. To develop a quantitative prediction model for Al NMR chemical shieldings in zeolites, we employed a kernel ridge regression (KRR) model, which has previously shown excellent accuracy in predicting chemical shieldings in aluminosilicate glasses. In the KRR model, the SOAP descriptors of aluminium atoms with a cutoff of 5 Å are used as input features, accurately capturing the local atomic environments.