In this study, the discharge coefficient of triangular weirs was modeled using the combination of the adaptive Neuro-fuzzy inference system (ANFIS) and the genetic algorithm (GA). In this study, the genetic algorithm was implemented for increasing the efficiency of ANFIS by adjusting membership functions as well as minimizing error values. in order to examine the ability of the hybrid model the Monte Carlo simulations were used. In this study, the k-fold validation method (k=5) was applied to evaluate the capability of the mentioned models. Then, using the input values, six hybrid ANFIS-GA models were introduced. Based on the analysis of the modeling results, the superior model predicted the value of the discharge coefficient in terms of the flow Froude number, the vertex angle of the weir, the ratio of the weir length to its height, the ratio of the flow head to the weir height and the ratio of the channel width to the weir length. The values of Mean Absolute Percent Error (MAPE) and Root Mean Square Error (RMSE) for the superior model were calculated 1.647 and 0.016, respectively. Furthermore, investigation of the numerical results indicated that the Froude number is the most effective parameter in modeling the discharge coefficient. Then, the results of the superior hybrid model were compared with the ANFIS results and it was concluded that the superior model simulates values of the discharge coefficient with higher accuracy. ©2019 Razi University-All rights reserved.