Deposition of barium sulfate (or BaSO4) has already been recognized as a devastating problem facing process industries and oilfield operations, mainly owing to its low solubility in aqueous solutions. Predicting and also preventing the overall damage caused by BaSO4 precipitation requires a profound knowledge of its solubility under different thermodynamic conditions. The main aim of this study is to develop a solubility prediction model based on a hybrid of least squares support vector nachines (LSSVM) and coupled simulated annealing (CSA) aiming to predict the solubility of barium sulfate over wide ranges of temperature, pressure and ionic compositions. Results indicate that predictions of the presented model are in well accordance with experimental measurements yielding overall correlation coefficient (R-2) of 0.996 and total RMSE of 0.00077. Constructed model was also found to outperform a previously well-known correlation employed for predicting BaSO4 solubility in aqueous solutions. Solubility predictions based on Pitzer ion interaction model were also compared to CSA-LSSVM predictions in terms of single point estimations and sensitivity analysis. Results of this study suggest that CSA-LSSVM model could be implemented in assessing the solubility of BaSO4 in Na-Mg-Ca-K-Ba-Cl-SO4-H2O system over wide ranges of temperature from 0 to 279 degrees C and pressures ranging from 1 to 1517 bars with an acceptable degree of accuracy. (C) 2014 Elsevier B.V. All rights reserved.