The temperature which the amount of saturated vapor pressure is equal to the actual pressure and due to decreasing it, the existence moisture of air also turns into a liquid is called dew point temperature. Additionally, owing to the shortage of water in whole world, water supplying to solve the initial requirements of human has become a global challenge. Due to the limited number of dew point measuring stations, employing the various approaches with acceptable accuracy is required. Recently, due to the significant ability of different soft computing (SC) methods in communizing between inputs and outputs, they have been considered by researchers to solve a wide range of problems. Therefore, in the present research, the performance of two SC methods namely support vector machine (SVM) and gene expression programming (GEP) for prediction of dew point temperature was investigated. Additionally, wavelet transform (WT) as a data preprocessing method was employed to improve the accuracy of models. Evaluation of models performance based on the statistical error benchmarks indicated that hybrid models outperformed the standalone SVM and GEP approaches. Moreover, the results illustrated that the accuracy of the hybrid model obtained by integration of WT and GEP (GEPW) was less than its integration with support vector machine (SVMW). Finally, among the proposed methods, SVM_RBF^(W-Haar) model was able to estimate the dew point temperature values with the highest accuracy with respect to NSE (0.98) and RMSE (0.85) and determined as the best model so as to estimate the dew point temperature.