The objectives of this study were to develop Quantitative Structure–Property Relationship (QSPR) models to predict Hildebrand solubility parameters (δt). The study investigated direct δt prediction by QSPR and indirect δt calculation from QSPR-predicted enthalpy of vaporization values. Molecular ground-state minimum energy conformations were identified and optimized using the AM1 semiempirical Quantum Mechanical (QM) method. Predictive multiple linear regression models were derived correlating training set experimental solubility parameters or enthalpies of vaporization with descriptors obtained from the QM calculations. A four-descriptor Hildebrand total solubility parameter regression equation was developed where R2=0.97, R=0.97, F=461.5, s2=0.53, and Root Mean Square Error (RMSE)=0.69. A four-descriptor QSPR model to predict enthalpies of vaporization (ΔHvap−kJ/mol) was constructed where R2=0.96, R=0.96, F=230.3, s2=4.75, and RMSE=2.04. δt calculations from ΔHvap correlated well with literature δt , where R2=0.96. Both of these models are statistically significant as well as predictive. The direct and indirect δt models developed here compare favorably with liquid solubility parameter estimation accuracies (10%) reported by Hoftyzer–Van Krevelen and Hoy. These methods are useful for determining the miscibility of small organic molecules, an important consideration when formulating polymer blends.