Water　quality　prediction　is　essential　for　effective　water　resource　management　and　pollution　prevention.　In　China,　research　on　predictive　analytics　for　various　water　bodies　has　not　kept　pace　with　environmental　needs.　This　study　addresses　this　gap　by　conducting　a　comprehensive　analysis　and　modeling　of　water　quality　monitoring　data　from　multiple　distributed　water　bodies　specifically　within　the　Yangtze　River　Delta.　Using　a　novel　approach,　this　paper　introduces　a　distributed　water　quality　prediction　system　enhanced　by　a　CNN-LSTM　joint　model.　This　model　synergistically　combines　convolutional　neural　networks　(CNN)　and　long　short-term　memory　(LSTM)　networks　to　robustly　extract　and　utilize　spatiotemporal　data,　thereby　significantly　improving　the　accuracy　of　predicting　dynamic　water　quality　trends.　Notably,　the　excellent　predictive　performance　of　the　joint　model　enables　its　prediction　results　to　achieve　RMSE　and　MAPE　as　low　as　1.08%　and　6.8%,　respectively.　Empirical　results　from　this　study　highlight　the　system＇s　superior　predictive　performance.　Based　on　these　findings,　this　paper　offers　targeted　recommendations　for　water　quality　monitoring,　treatment,　and　management　strategies　tailored　to　the　specific　needs　of　the　Yangtze　River　Delta.　These　contributions　are　poised　to　aid　policymakers　and　environmental　managers　in　making　more　informed　decisions.