This study introduces an approach for calculating the free flow and submerged flow through flat-trapezoidal gate. The flow calculation model was established based on BP (Back Propagation) neural network and RBF (Radial Basis Function) neural network, with multiple variables and combinations as well as single output. The input variables for the model included slope coefficient, gate opening, total head in front of the gate, hydraulic radius, contraction depth behind the gate, and downstream channel water depth. The output variable was measured flow rate. The model was trained and tested using experimental data, and extensive validation confirmed that both BP and RBF artificial neural network models demonstrated strong predictive performance. These models exhibited excellent adaptability and high accuracy in predicting flow rates for trapezoidal gates in canal systems in irrigation areas, thereby enabling precise flow control.