The abnormal distribution of infiltration lines poses a serious threat to the stability and safety of earth and rock dams. Therefore, accurate prediction of the infiltration line has become a core task in the safety monitoring of earth and rock dams. However, there are nonlinear and non-smoothness problems in the monitoring data of the pressure tube, which makes the prediction model of the dip line prone to overfitting, which in turn affects the prediction accuracy. In order to solve this problem, a dip line prediction method based on SSA-LSTM model was proposed. The method combines SSA (sparrow search algorithm) and LSTM (long-short-term neural network) to improve the prediction accuracy by optimizing the initial learning rate and regularization parameters of the model to better match the input data with the network structure. The model prediction results were evaluated by three quantitative evaluation metrics, namely, coefficient of determination (R²), MAE (mean absolute error) and RMSE (root mean square error). The results show that the prediction accuracy of the SSA-LSTM model is significantly improved compared with the traditional LSTM and other models, which provides a valuable reference for the prediction of the dip line of earth and rock dams.