Promoting sustainable mining practices while safeguarding water ecosystems demands precise anticipation of mine water influx. This investigation pioneers a novel approach harnessing microseismic monitoring to detect water-conducting conduits and elevate proactive response strategies. Through the utilization of microseismic energy density analysis, fracture points within rock formations are continuously monitored, offering real-time insights. Nonetheless, the data generated from this method often exhibits fragmentation, sporadic patterns, and data heterogeneity, complicating the identification of evolving water-conducting pathways. To surmount this challenge, we have seamlessly integrated the self-attention mechanism into the long short-term memory (LSTM) model, resulting in the innovative SA-LSTM fusion. This hybrid model predicts the following day’s water inflow, effectively merging data from microseismic monitoring with groundwater levels. This fusion facilitates a robust correlation between monitoring data and water inflow metrics. Comparative assessments underscore the SA-LSTM’s superiority over other intricate time series models in terms of forecast precision, with a mean absolute error(MAE) of 21.8 m3/h, an RMSE of 39.3 m3/h, and an MAPE of 2.8% in the test stage of the water inflow event. By amalgamating diverse datasets, it substantially enhances the accuracy of predicting water inflow within coal mines. The discernments from this study not only introduce more accurate water inflow predictions but also provide technical guidance for the safety production of mine.