Machine learning(ML)has demon-strated significant potential in en-hancing the predictive capabilities of density functional theory methods.In this study,we develop an ML model for correcting B3LYP-D,a density functional approximation that incorporates dispersion correc-tions for non-covalent interactions.This model utilizes semilocal elec-tron density descriptors,and is trained with accurate reference data for both relative and ab-solute energies.Extensive benchmark tests reveal that the ML correction substantially en-hances the generalization ability of the B3LYP-D functional,improving the predictions of at-omization and dissociation energies for complex molecular systems.It retains the accuracy of B3LYP-D in predicting reaction barrier heights and non-covalent interactions while enabling efficient,fully self-consistent field calculations.This work signifies a promising advancement in the development of ML-corrected functionals that surpass the performance of traditional B3LYP-D.