The outflow via the weir gate in coastal estuaries is affected by factors, including channel shape, upstream inputs, sluice gate operations, and tidal variations, leading to nonlinear and transitory correlations between the water stage and discharge. The most common technique utilized to calculate discharge is the weir gate overflow equation. Nonetheless, the significant dynamic fluctuations in upstream and downstream water level differentials during the opening or closing of the gate render the exclusive use of static water level differences inadequate for formulating a connection equation that satisfies accuracy standards. This research proposes a dynamic trend prediction approach that utilizes time-series data of water levels and discharge, accounting for temporal trend variations, as input for simulation with a three-layer backpropagation neural network. In the tidal portions of the Lixia River basin, the correlation coefficients for the discharge of four harbor gates surpassed 0.8, and the mean error diminished to 3.00%. It significantly boosts the fitting accuracy of the results and improves data precision during the transition between gate opening and closure. The novel approach employs intelligent algorithm theory to analyze harbor gate flow, offering a more scientific and accurate representation of the gate’s overflow capacity.