The use of machine learning (ML) across the vast catalogue of chemical problems has increased substantially in recent years given the potential for both rapid and accurate predictions with research areas including solvation effects, 1 yield predictions, quantum chemical property prediction, machine learned forcefields, and high-level energy predictions. One area in which ML has delivered accurate and computationally efficient results is in the prediction of reaction barriers derived from, for example, density functional theory (DFT) or coupled cluster (CC) calculations. Various ML techniques have been employed to into these energy components can provide insight into the factors that govern reactivity and is a particularly popular method of analysis in the study of cycloadditions. As an example, in a study of the reaction of cycloalkenones with various cyclic dienes, it was found that the interaction energies across the reaction series were almost identical. Therefore, the differences in reaction barrier arise from changes in dienophile and diene distortion energies. In a related study of the cycloaddition reaction of cyclopropenes with butadiene, the distortion energies of the endo and exo reactions were nearly identical, with the differences in reaction barrier (endo reactions favoured by 2-3 kcal mol -1 ) arising from the difference in interaction energies. This type of analysis therefore provides detailed information on reactivity trends which can aid in the rational design of new reactions. Similar analysis has been applied to many other reactions such as: SN2/E2, iridium-catalysed C-H borylation, palladiumcatalysed cross-coupling, asymmetric propargylation and allyboration, and nickel-catalysed amide (a functional group that is ubiquitous in drug discovery) C-N bond activation. However, computing accurate distortion and interaction energies requires the use of expensive quantum mechanical calculations (typically DFT) which prevents the routine use of this reactivity analysis on large datasets. ML may instead offer a rapid approach to high accuracy distortion and interaction energy predictions. In this work, we report the first example of predicting distortion and interaction energies using ML. Models are built to predict DFT energies from rapid semi-empirical quantum mechanical (SQM) calculations for four distinct datasets (of which one is a new dimethyl malonate Michael addition dataset). The models yield high accuracy distortion and interaction energies for all four datasets with further insight available from the computed SQM TSs. Finally, we show that performance remains high when the models are applied to two unseen reaction sets from the literature.