The water level changes in the estuarine area are influenced by various factors with different mechanisms and periodicities, including runoff, astronomical tides and storm surges, resulting in relatively low forecasting accuracy of the residual water level. To improve the forecast accuracy of residual water levels, an estuary-tidal residual water level forecasting method based on VMD-BPNN (variational mode decomposition and back propagation neural network) is proposed. By conducting tidal harmonic analysis on the long-term water level data of estuarine areas, astronomic water levels and residual water levels can be obtained. The residual water level is subjected to VMD, obtaining multiple intrinsic mode functions of the residual water level in the time series. Then, the BPNN is used to train each intrinsic mode function, and an accurate forecast of residual water levels in the estuary area is achieved through the forecast and superposition of each intrinsic mode function. Water level data from four typical tidal stations in estuarine areas of the United States and France were used for experimental analysis. The method was verified by using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency (NSE) as evaluation indicators, and the results showed that it had a good comprehensive performance, and high stability and accuracy in the forecasting of the residual water level. This study thereby provides a valuable foundation and insightful reference for future research into the complex mechanisms driving water level changes and the development of high-precision tidal forecasting systems in estuarine environments.