Accurate prediction of mine water inflow is a key technology to ensure safe production and ecological protection of mines. In view of the fact that traditional prediction methods are difficult to handle complex spatiotemporal characteristics, and the existing data-driven models have problems such as single structure and insufficient parameter optimization, a temporal convolutional network-bidirectional long short-term memory network (TCN-BiLSTM) joint prediction model was proposed based on simulated annealing algorithm optimization. This model combines the multi-scale feature extraction capability of the temporal convolutional network (TCN) and the context-dependent modeling advantages of the bidirectional long short-term memory network (BiLSTM), and combines the simulated annealing algorithm (SA) for global optimization of hyperparameters to construct an efficient SA-TCN-BiLSTM prediction framework. Based on the mine water inflow data and hydrological and meteorological observation data of Shenhua Shangwan Mining Area from 2014 to 2023, the model is compared with benchmark models such as long short-term memory (LSTM), BiLSTM, and TCN. The results show that the water inflow in the mining area shows significant seasonal changes from 2014 to 2023, and precipitation, temperature and potential evaporation have non-balanced contributions to the prediction of water inflow. The proposed SA-TCN-BiLSTM model is significantly better than the traditional method in prediction accuracy, with an mean square error(MSE) of 2.10 and a mean absolute percentage error(MAPE) of 0.34%. Compared with the unoptimized TCN-BiLSTM model, the prediction accuracy is improved by 97.5%. The model predicts that the water inflow in the next 12 months will fluctuate in the range of 250~430 m³/h and reach the annual peak during July and September. This prediction trend is highly consistent with historical observation data and regional hydrological characteristics. The research results provide reliable technical support and scientific basis for the prediction of water inflow in mining areas and the formulation of water prevention and control plans.