To　detect　leakage　in　urban　water　distribution　networks　and　study　the　relationship　between　monitoring　information　and　leakage　diagnosis,　the　XGBoost　algorithm　was　applied　to　identify　the　leakage　zone　and　predict　the　leakage　level.　Software　was　adopted　to　call　EPANET　V2.2　for　analyzing　a　water　distribution　network　model,　and　emitters　were　added　in　the　middle　of　pipes　to　simulate　leakage　events.　By　changing　the　discharge　coefficient,　the　leakage　flow　was　varied　in　hydraulic　analysis.　The　node　pressure　sensitivity　matrix　was　calculated,　and　the　sensor　placement　and　pipe　zones　were　determined　using　the　fuzzy　c-means　clustering　method.　Different　leakage　scenarios　were　simulated,　and　the　location　and　level　of　leakage　were　identified　through　pressure　changes　at　monitoring　points　based　on　the　XGBoost　algorithm.　Taking　two　hydraulic　models　of　water　distribution　network　as　examples　to　simulate　and　predict,　which　were　compared　with　back-propagation　neural　network　algorithm,　it　was　revealed　that　the　XGBoost　algorithm　can　not　only　identify　the　leakage　zone,　but　also　predict　the　leakage　level　well.　On　the　basis　of　sensor　placement　by　Fuzzy　c-means　algorithm,　for　different　leak　scenarios,　the　average　identification　accuracy　of　the　XGBoost　algorithm　was　5.54%　higher　than　that　of　the　back-propagation　neural　network　algorithm　in　leakage　zone.　The　average　prediction　accuracy　of　the　XGBoost　algorithm　was　2.71%　higher　than　that　of　the　back-propagation　neural　network　algorithm　in　leakage　level.　The　XGBoost　algorithm　effectively　can　identify　the　leakage　of　water　distribution　networks.　(C)　2021　American　Society　of　Civil　Engineers.