In this work, we present a hybrid SQM-ML approach for the prediction of distortion and interaction energies for four unique datasets across two reaction classes which provides a significant improvement upon the pre-ML MAEs between AM1 and DFT calculations. We have shown that predictions below the 1 kcal mol -1 accuracy threshold are possible for distortion and interaction energies for the Diels-Alder dataset; Michael addition predictions are either approaching 1 kcal mol -1 or are below. The accuracy of predictions for the [3+2] cycloaddition distortion and interaction energies are similar to barrier prediction accuracies reported in the literature (2-3 kcal mol -1 ). We reviewed our model performances as a percentage of the test set range to better compare accuracy between datasets. This showed that while there is still room to improve model performance for the [3+2] cycloaddition dataset in terms of the MAE, the performance matches well with other datasets tested. Finally, we used our SVR models built on ds2 to accurately predict the distortion and interaction energies for two unseen, high impact literature datasets of Diels-Alder reactions that use the distortion/interaction model for reactivity analysis. Fig. -External test sets for ds2 ML models constructed from references 33 and 37. The dienes in both datasets are part of ds2 thus diene distortion is not predicted. Any dienophiles that were in ds2 were removed from these external test sets.