Thermal risk assessment is very important in the primary stages of chemical compound development. In this study, a model to estimate the self-accelerated decomposition temperature of organic peroxides was developed. The structural information of compounds was used to calculate descriptors, on which partial least-squares (PLS) regression and support vector regression were applied for temperature prediction. Molecular mechanics and density functional theory calculations were performed before descriptor calculations, for structure optimization, using a genetic algorithm for variable selection. Structure optimization and variable selection immensely improved the prediction accuracy. Thus, a PLS model, with R2 = 0.95, root mean square error = 5.1 °C, and mean absolute error = 4.0 °C, exhibiting higher accuracy than existing self-accelerating decomposition temperature prediction models, was constructed.