A Comprehensive Review of Machine Learning Applications in Wastewater Treatment: Current State, Comparative Analysis, and Future Directions

Abstract

As the world’s need for clean water keeps rising and pollution continues to worsen, there is a growing push for better wastewater treatment systems. Treatment plants (WWTPs) are essential not only for protecting public health but also for keeping the environment safe. Still, running these plants is not easy because the quality of incoming water often changes, the biological processes are complex, and regulations are very strict. Traditional methods usually fall short, being slow and inefficient. Newer approaches, like machine learning (ML) and artificial intelligence (AI), bring fresh opportunities by making it possible to predict issues in real time, spot irregularities, improve processes, and support better decision-making. This literature review brings together findings from five key research papers and over 40 additional studies published between 2018 and 2025. The review highlights a significant shift towards advanced deep learning (e.g., LSTM, GRU) and ensemble models, demonstrating superior performance in capturing complex, time-dependent data. Key trends include multi-source data fusion, expanding focus on effluent quality prediction for regulatory compliance, nutrient removal, energy optimization, and predictive maintenance. Despite these advancements, persistent challenges include data quality and availability, model interpretability ("black box" nature), generalizability across diverse WWTPs, and integration with existing infrastructure. Future research directions emphasize hybrid and physics-informed models, Explainable AI (XAI), Digital Twins, Reinforcement Learning for optimal control, and fostering interdisciplinary collaboration. Ultimately, ML/AI holds immense potential to revolutionize wastewater management, transitioning from reactive to proactive strategies, contingent on addressing these critical limitations for widespread and sustainable adoption