Experimental determination of the explosive properties of chemicals is a tedious, time- and resource-intensive, and involved risk. In this study, we have established a three-tier structure property relationship (SPR) modeling strategy for screening the chemicals for their explosive behavior. Accordingly, qualitative (ternary classification: nonexplosive, ideal and nonideal explosive), semiquantitative (binary classification: industrial and high explosive), and quantitative SPR models based on decision tree forest (DTF) and decision tree boost (DTB) algorithms were developed for discriminating the chemicals in different categories and predicting the detonation velocity (DV) of ideal and lower flammability limit (LFL) of nonideal explosives in accordance with the OECD guidelines. The statistical quality and external predictive power of the developed SPR models were evaluated through the internal and external validation procedures. In the test set, the qualitative and semiquantitative SPR models (DTF, DTB) rendered accuracy of >99%, while the quantitative SPR models (DTF, DTB) for ideal and nonideal explosives yielded correlation (R-2) of >0.93 and >0.94 between the measured and predicted DV and LFL values, respectively. Values of various statistical validation coefficients derived for the test data were above their respective threshold limits and thus put a high confidence in this analysis. The applicability domains of the constructed SPR models were determined using the descriptors range, Euclidean distance, and leverage approaches. The SPR models in this study performed better than the previous studies. The results suggest that the developed SPR models can reliably predict the explosive properties of diverse chemicals and can be useful tools for the screening of candidate molecules in the future development process.