With　the　development　of　urbanization,　the　accurate　prediction　of　effluent　quality　has　become　increasingly　critical　for　the　real-time　control　of　wastewater　treatment　processes.　The　conventional　method　for　measuring　effluent　biochemical　oxygen　demand　(BOD)　suffers　from　significant　time　delays　and　high　equipment　costs,　making　it　less　feasible　for　timely　effluent　quality　assessment.　To　tackle　this　problem,　we　propose　a　novel　approach　called　En-WBF　(ensemble　learning　based　on　weighted　BoostForest)　to　predict　effluent　BOD　in　a　soft-sensing　manner.　Specifically,　we　sampled　several　independent　subsets　from　the　original　training　set　by　weighted　bootstrap　aggregation　to　train　a　series　of　gradient　BoostTrees　as　the　base　models.　Then,　the　predicted　effluent　BOD　was　derived　by　weighting　the　base　models　to　produce　the　final　prediction.　Experiments　on　real　datasets　demonstrated　that　on　the　UCI　dataset,　the　proposed　En-WBF　approach　achieved　a　series　of　improvements,　including　by　28.4%　in　the　MAE,　40.9%　in　the　MAPE,　29.8%　in　the　MSE,　18.2%　in　the　RMSE,　and　2.3%　in　the　R2.　On　the　Fangzhuang　dataset,　the　proposed　En-WBF　approach　achieved　a　series　of　improvements,　including　by　8.8%　in　the　MAE,　9.0%　in　the　MAPE,　12.8%　in　the　MSE,　6.6%　in　the　RMSE,　and　1.5%　in　the　R2.　This　paper　contributes　a　cost-effective　and　timely　solution　for　wastewater　treatment　management　in　real　practice　with　a　more　accurate　effluent　BOD　prediction,　validating　the　research　in　the　application　of　ensemble　learning　methods　for　environmental　monitoring　and　management.