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Prediction of river flow is an important issue in planning water resources and management of supply and demand in future conditions. Hence, it has attracted researchers’ notice due to its importancein the designing, planning, management and operation of water facilitiesand also management the critical conditions such as flood and drought. In the present study, it was attempted to improve Zarinehrud river inflow prediction for use in water resource planning using a hybrid approach based on gamma test and supporting vector machine model (GSVM). For this purpose, the best possible combination of predictors was selected from the different combinations of 10 meteorological and hydrological variables in the basin. Then, based on the best combination of predictors, the potential of river inflow was predicted using a support vector machine. Comparison of predicted and observed flow indicated the good performance of hybrid approach in prediction of potential river inflow for application in basin management plans. In this case, the overall accuracy of the model to predict drought management levels based on Zarinehrud flow is 71.4%, and the upper and under estimation error are 8.2 and 20.4% respectively. These results show the acceptable precision of GSVM model for flow prediction in different hydrological situations of basin. Application of Hybrid Gamma-SVM Approach for River Flow Prediction in Zarinehrud Basin