One way to improve the infrastructure, operations, monitoring, maintenance, and management of wastewater treatment systems is to use machine learning modelling to make smart forecasting, tracking, and failure prediction systems. This method aims to use industry data to treat the wastewater treatment model. Gradient-Boosted Decision Tree (GBDT) algorithms were used gradually to predict wastewater plant parameters. In addition, we used the Slime Mould Algorithm (SMA) for feature extraction and other acceptable tuning procedures. The input and effluent Chemical Oxygen Demand (COD) prediction for effluent treatment systems applies to the GBDT approaches employed in this study. GBDT-SMA employs artificial intelligence to provide precise method modelling for complex systems. Several training and model testing techniques were used to determine the best topology for the neural network models and decision trees. The GBDT-SMA model performed best across all methods. With 500 data, GBDT-SMA achieved an accuracy of 96.32%, outperforming other models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and K-neighbours RF, which reached an accuracy of 82.97, 87.45, 85.98, and 91.45%, respectively.

改善废水处理系统的基础设施、运营、监控、维护和管理的一种方法是使用机器学习模型来制作智能预测、跟踪和故障预测系统。该方法旨在利用行业数据来处理废水处理模型。梯度提升决策树（GBDT）算法逐渐用于预测污水处理厂参数。此外，我们还使用史莱姆霉菌算法 (SMA) 进行特征提取和其他可接受的调整程序。废水处理系统的输入和废水化学需氧量 (COD) 预测适用于本研究中采用的 GBDT 方法。GBDT-SMA 采用人工智能为复杂系统提供精确的方法建模。使用多种训练和模型测试技术来确定神经网络模型和决策树的最佳拓扑。GBDT-SMA 模型在所有方法中表现最佳。在 500 个数据的情况下，GBDT-SMA 的准确率达到了 96.32%，优于其他模型，如人工神经网络 (ANN)、卷积神经网络 (CNN)、深度卷积神经网络 (DCNN) 和 K-neighbours RF，均达到了准确率分别为 82.97、87.45、85.98 和 91.45%。