The solubility parameter is considered to be a significant parameter for the chemical industry. In this study, the quantitative structure property relationship (QSPR) method is applied to develop three models for determination of the solubility parameters of pure nonelectrolyte organic compounds at 298.15 K and atmospheric pressure. To propose comprehensive, reliable, and predictive models, about 1400 data belonging to experimental solubility parameter values of various nonelectrolyte organic compounds are studied. The genetic function approximation (GFA) mathematical approach is applied for selection of proper model parameters (molecular descriptors) and to develop a linear QSPR model. To study the nonlinear relations between the selected molecular descriptors and the solubility parameter, two approaches are pursued: the three-layer feed forward artificial neural networks (3FFANN) and the least square support vector machine (LSSVM). Furthermore, the Levenberg-Marquardt (LM) and genetic algorithm (GA) optimization methods are respectively implemented to optimize the 3FFANN and LSSVM models. Consequently, we obtain three predictive models with satisfactory results quantified by the following statistical parameters: absolute average relative deviation (AARD) of the represented/predicted properties from existing experimental values by the GFA linear equation of 4.6% and squared correlation coefficient of 0.896; AARD of the QSPR-ANN model of 3.4% and squared correlation coefficient of 0.941; and AARD of 3.1% and squared correlation coefficient of 0.947 evaluated by the QSPR-LSSVM model.