Abstract: The height of the fracture zone in mined overburden rock determines the gas extraction parameters of the final hole or roadway of the gas extraction borehole. In order to further improve the prediction accuracy, by collecting 361 sets of data from different mining areas, the relationship between the height of the mining-induced fissure zone and parameters like mining height, coal seam inclination angle, working face oblique length, mining depth, and hard rock lithology proportion coefficient was analyzed. Three machine learning algorithms, including deep belief network (DBN), long short-term memory network (LSTM), and Elman neural network (ENN), were used to perform five-fold cross-validation on the height of mining-induced fissure. LSTM was selected as the preliminary prediction model based on the common evaluation indicators such as coefficient of determination, root mean square error, mean absolute error, mean absolute percentage error, etc. The LSTM prediction model of mining-induced fissure was optimized by genetic algorithm (GA) and sparrow search algorithm (SSA), and the prediction results of LSTM, GA-LSTM and SSA-LSTM were obtained. The results show the SSA-LSTM prediction model has the better prediction results than the LSTM and GA-LSTM prediction models. Its coefficient of determination, root mean square error, average absolute error, and average percentage error are 0.991, 0.329, 0.148, and 0.017 respectively. All accuracy evaluation indicators meet the prediction accuracy judgment requirements. The constructed mining fracture zone height prediction model has resonable high accuracy and adaptability certain universality.