Prediction　of　water-conducting　fractured　zone　(WCFZ)　of　mine　overburden　is　the　premise　for　reducing　or　eliminating　water　inrush　hazards　in　undersea　mining.　To　obtain　a　more　robust　and　precise　prediction　of　WCFZ　in　undersea　mining,　a　WCFZ　prediction　dataset　with　122　cases　of　fractured　zones　was　constructed.　Five　machine　learning　algorithms　(linear　regression,　XGBRegressor,　RandomForestRegressor,　LineareSVR,　and　KNeighborsRegressor)　were　employed　to　develop　five　corresponding　predictive　models,　taking　multiple　factors　into　account.The　optimal　parameters　for　each　model　are　obtained　through　ten-fold　cross-validation　(10CV).　The　model＇s　predictive　performance　was　validated　and　assessed　using　two　metrics,　namely　the　coefficient　of　determination　(R2)　and　mean　squared　error　(MSE).　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,　showing　a　high　R2　value　of　up　to　0.925　and　a　low　MSE　value　of　3.61.　The　proposed　model　was　validated　in　a　recently　established　mining　area　on　Sanshan　Island,　China.　It　shows　low　absolute　and　relative　errors　of　0.71　m　and　2.01%,　respectively,　between　the　predicted　value　from　the　model　and　observation　result　from　the　field,　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.