Broad-crested weirs are structures used to measure and control the water flows in rivers, canals, and irrigation and drainage networks. Accurate estimation of spillway discharge is one of the most striking elements in measurement structures. So far, many researchers have studied this issue based on various experimental conditions and a specific range of optional variables. They also have presented several relations. In the present study, 113 data sets of Bos were used for applicability of Artificial Neural Network (ANN), Gene expression programming (GEP), regression models to estimate the discharge coefficient for the rectangular broad-crested weirs. The effectiveness of the models was calculated using statistical criteria, including the coefficient of determination (R²), Root Mean Square Error (RMSE), and mean absolute error (MAE). Comparing the models showed that the ANN with the highest R² coefficient (0.9916), lowest RMSE = 0.0012, and MAE = 0.00052 has the best discharge coefficient estimation than GEP models, regression models, and other empirical relations for the rectangular broadcrested weirs.