We carried out comprehensive machine learning (ML) simulations to investigate the molecular origin of the impact sensitivities of 53 nitroaromatic explosives characterized by ℎ .# values. After an initial screening of 42 ML algorithms, the four with the smallest RMSE of the predicted ℎ .# values, namely, Extra Trees, Random Forests, Gradient Boosting, and AdaBoost, were further investigated. The algorithms used four features (i.e., properties), and were run with a training set of 42 molecules and a test set comprising 11 molecules involving the extensive range of sensitivities 25 cm ≤ ℎ .# ≤ 320 cm. Three electronic features were atomic-centered DMA electric multipoles that characterize accurately in detail the density functional DFT charge density of the explosive. The fourth was a structural one, the number of the explosophore nitro groups.