Prediction of water-conducting fractured zone (WCFZ) of mine overburden is the premise for reducing or eliminating water inrush hazards in undersea mining. This study constructs a WCFZ prediction dataset containing 122 cases of fractured zones. By introducing five machine learning algorithms, five predictive models for WCFZ considering multiple factors are established. The optimal parameters for each model are obtained through ten-fold cross-validation. The model's predictive performance was validated and assessed using two metrics, namely the coefficient of determination and mean squared error. A comparison was made with the regression performance of commonly used empirical formulas. The results indicate that the constructed model outperforms reliance solely on theoretical criteria. The proposed model was validated in a recently established mining area on Sanshan Island, China, demonstrating a high level of consistency with on-site conditions. This paves a path to leveraging machine learning algorithms for predicting the height of WCFZ.