# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 6 Conclusions  
This comprehensive review demonstrates that technologies associated with machine learning signify the kind of paradigm shift we rarely see in the optimization of wastewater treatment. These are, indeed, powerful new tools for achieving the kinds of capabilities we want in terms of monitoring, predicting, and controlling these very complex treatment systems. The analysis shows that predictive supervised learning models have become key tools in establishing the predictive nature of important parameters related to the quality of wastewater, like BOD and COD. Of these strategies, hybrid models are the most promising solution. The computational strength of neural networks is combined with the comprehensibility of fuzzy logic systems. This combination tackles one of the field's most persistent challenges: ensuring that both accurate predictions and transparent, trustworthy decision-making processes are available for operators. Integrating digital twin technology and IoT systems within machine learning frameworks is a breakthrough for real-time monitoring capabilities. By bringing together these technologies, the smart wastewater treatment facilities of the future will be able to develop responsive and adaptive operational frameworks. The widespread implementation of these methods faces several critical challenges that must be addressed. Data quality and availability are fundamental limitations. High-quality training datasets are a necessity for the performance of machine learning models. Yet many treatment facilities either do not have such datasets or have datasets that are not suitable for this purpose. Integrating systems is a significant challenge because current wastewater treatment plants usually must be altered substantially to accommodate new, advanced, and machine-learning-driven technologies. This includes not only compatibility factors but also economic ones. Upgrading a plant involves huge financial investments, and the justification for theseinvestments usually must be based on either performance improvements or favorable cost-benefit ratios. High implementation cost is the primary barrier to ML adoption. Upgrades on present WWTPs, on the other hand, are very costly in terms of new sensors, computing infrastructure, software licensing, and system integration. Unlike the ecological benefits that can be proven in pilot studies, economic sustainability requires long-term investments whose returns are not yet known. While this study shows that ML systems could lower energy usage and chemical consumption, the economic justification is complex due to the highly variable operational conditions, and plant managers may not be able to estimate savings accurately. Payback period uncertainty introduces financial risk that many reject. Once translated into economic value, in the form of avoided penalties, these environmental benefits can be calculated. Ultimately, whilst the environmental performance is likely to be the key long-term desideratum, economic viability represents the primary barrier to the widespread adoption of ML in wastewater treatment and is therefore the most important and challenging to overcome. For data quality, we advocate implementing standardized sensor networks, including automated quality checks and industry-level databases monitoring for a minimum of 2 years. Regarding transparency hurdles, creating explainable AI models with SHAP and LIME tailored for wastewater operators can overcome this problem. Economic implementation barriers can be overcome with phased deployment strategies that deliver increasing value rather than requiring full up-front costs. Future research should prioritize the following:  
1. Standardization: developing a standardized evaluation framework with well-defined performance metrics and cross-validation. 2. Software for edge computing devices to develop ML models and model deployment with specific hardware recommendations. 3. Evaluation: multi-site validation across 10+ geographically diverse WWTPs using the same set of hardware across sites. 4.