Predicting detonation behavior — often called the explosive’s “performance fingerprint” — has long been a bottleneck in engineering design, constrained by safety, cost, and time. Traditional testing is not only hazardous but also slow and expensive. Deep learning offers a data-driven approach for predicting detonation velocity, pressure, and Jones-Wilkins-Lee (JWL) EOS coefficients. However, most deep learning models operate as black boxes and do not provide physical interpretability. To address this limitation, this work evaluates the physics based EMDB 2.1 platform, a thermodynamic modeling framework that calculates detonation parameters from molecular composition and charge density, and compares its predictions with those of a deep learning model. Both the deep learning model and EMDB 2.1 deliver engineering-grade accuracy in predicting detonation pressure and velocity. Notably, EMDB 2.1 consistently outperforms the deep learning model, especially in pressure (root mean square error (RMSE): 0.8 GPa vs. 1.2 GPa) and velocity (RMSE: 0.10 km/s vs. 0.14 km/s). When predicting JWL EOS parameters, both methods generate isentropic expansion curves for the three benchmark explosives TNT, HMX, and Octol (HMX 78%/TNT 22%) that are in reasonable agreement with the reference curves derived from experimental data. Although the individual JWL parameters (A, B, C, R₁, R₂, and ω) differ numerically between the two models, their resulting curves show similar behavior for these cases.