Precipitation of strontium sulfate (or SrSO4) has already been distinguished as one of the most costly and critical problems which may occur in process industries and oilfield operations. Costs due to scaling and remedial actions that need to be taken afterward are generally high owing to low solubility of SrSO4 in aqueous solutions. Therefore, a thorough understanding of the SrSO4 thermodynamic behavior under various operating conditions is vital to predict or even avoid the overall damage caused by scaling. The primary aim of this work is to develop a model based on Least Squares Support Vector Machine (LSSVM) and Coupled Simulated Annealing (CSA) referred to as CSA-LSSVM to predict strontium sulfate solubility as a function of pressure, temperature and ionic compositions. In this context, we have employed almost 1641 experimental data regarding strontium sulfate solubility to build a comprehensive model and to evaluate its reliability. The results show that the proposed model has a better performance in comparison with pre-existing empirical correlations used for predicting SrSO4 solubility and it is also in well accordance with experimental measurements. A comparison was also drawn between predictions of Pitzer ion interaction model with CSA-LSSVM model forecasts in terms of sensitivity analysis and single point solubility predictions. The whole dataset was analyzed at the end to diagnose the possible outliers and to investigate the reliability of recorded measurements. Based on the results obtained from this study, developed model could successfully be used in predicting SrSO4 solubility in aqueous Na-Ca-Mg-Sr-Cl-SO4-H2O system over temperature ranges from 2 to 253.5 degrees C, and pressures from 1 to 568.51 atm. (C) 2014 Elsevier B.V. All rights reserved.