In this study, we systematically explored and compared the effectiveness of three advanced data augmentation strategies-transfer learning, delta learning, and feature engineering-in enhancing the prediction accuracy of activation energies using the D-MPNN model. Each method utilized low-level computational data generated by SQM methods, specifically GFN2-xTB, to improve model performance when high-level data availability is limited.