Finding an accurate computational method for estimating pan evaporation (EPm) can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called multiple model-support vector machine (MM-SVM) with the aim of showing the increasing, decreasing, and constant accuracy behavior of this hybrid model and improving the results of estimating EP compared to the two models ANN and SVM on a monthly scale of EPm in four meteorological stations (Ardabil, Khalkhal, Manjil (from Iran), and Grand Island (from the USA)) located in semi-arid regions, using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)), was evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE), and coefficient of determination (R-2)) and with the help of case visual indicators were compared. According to the results of evaluation indicators in the test phase, MM-SVM-6, ANN-5, MM-SVM-3, and MM-SVM-7 with RMSE = 1.088, 0.761, 0.829, and 0.134 mm/day; MAE = 0.79, 0.54, 0.589, and 0.105 mm/day; KGE = 0.819, 0.903, 0.972, and 0.981; and R-2 = 0.939, 0.962, 0.967, and 0.996 and with four input variables were introduced as the best models in Ardabil, Khalkhal, Manjil, and Grand Island stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs estimated by independent models, its power to estimate EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant, and decreasing modes in EPm estimation accuracy by this hybrid model under the semi-arid climatic conditions of the studied areas were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.