In China, the Loess Plateau's fragile geological structure leads to complex and variable surface subsidence in old gob areas following coal mining activities. Accurately predicting this residual subsidence remains a significant scientific challenge. In this study, a method for residual subsidence prediction using an Exponential Smoothing Long Short-Term Memory (EsLSTM) model is proposed. The investigation centers on the 18,001# old goaf area of the Yangquan Coal Mine in Shanxi Province. Using Sentinel-1A imagery, continuous SAR data from 98 periods were acquired and processed via Enhanced Distributed Scatter InSAR technology. The EsLSTM model was then developed to capture the subsidence time-series characteristics of all surface scatter points and predict future ground subsidence. The analysis reveals that the EsLSTM model delivered excellent accuracy, achieving an R 2 value of 0.975. It also outperformed SVR and traditional LSTM models, with a Mean Absolute Error of 2.2 mm and a Root Mean Square Error of 7.9 mm. Predicted results indicate that by October 2023, the maximum cumulative subsidence at the 18,001# working face of the Yangquan Coal Mine will reach 204 mm. The subsidence trend is expected to become more gradual and stable, suggesting a low likelihood of geological disasters in the area.