Wax deposition is a serious problem during oil production in the petroleum industry. Therefore, accurate prediction of this solid deposition problem can result in increasing the efficiency of oil/gas production. In this article, a novel approach is proposed to develop a predictive model for the estimation of wax deposition. An intelligent reliable model is proposed using a robust soft computing approach, namely, least-squares support vector machine (LSSVM) modeling optimized with the coupled simulated annealing (CSA) optimization approach. Our results demonstrate that there is good agreement between predictions based on the CSA-LSSVM model and experimental data on wax deposition. Furthermore, the performance of the newly developed model is compared with the performance of neural network and multisolid models for predicting wax deposition. The results of this comparison indicate that the proposed method is superior, in terms of both accuracy and generality, to the neural network and multisolid models. Finally, to check whether the newly developed CSA-LSSVM model is statistically correct and valid, the leverage approach, in which the statistical Hat matrix, the Williams plot, and the residuals of the model results lead to the identification of probable outliers, is applied. It is found that all of the wax deposition experimental data used in the present study seem to be reliable and that only one point is outside the applicability domain of the developed models for wax deposition.