# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 3 Materials and Methods
### Objectives and Purpose of the Review  
The essential aim of this systematic review is to recognize and assess the methods involving machine learning and deep learning that are presently being utilized in processes of wastewater treatment, with a certain focus on their efficacy, the difficulties in making them work, and how they could be used to make treatment operations better.  
This review answers three principal research queries, which serve as a compass for navigating a thorough analysis of artificial intelligence uses in wastewater management.  
The primary research question is pinpointing which ML methods are most effectively used in wastewater treatment optimization (which required an in-depth analysis of the specialized literature using Web of Science, Scopus, IEEE Xplore, PubMed, and arXiv). This means an in-depth look at not just supervised learning methods but also at hybrid and ensemble models that appear to be used more in the last few years.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Journal** & **2020** & **2021** & **2022** & **2023** & **2024** & **2025** & **Total** \\ \hline Environmental Monitoring and Assessment & 1 & 1 & & & 2 & 4 \\ \hline Environmental science and pollution research & 1 & 2 & & & 3 \\ \hline Processes & & & & 1 & 2 & 3 \\ \hline Atmosphere & & & & 1 & 1 & 2 \\ \hline Electronics & & 2 & & & 2 \\ \hline Italian National Conference on Sensors & & & 2 & & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 7: Citation relationships of academic articles that were selected using the method described in Figure 5.  
The second research question investigates the main uses of these AI technologies, especially in the contexts of the following:  
* Real-time monitoring and control systems;
* Predictive modeling for key wastewater quality parameters like COD and BOD;
* Process optimization and automation;
* The integration of these systems with IoT and digital twin technologies.  
The third research question targets the essential challenges, limitations, and gaps encountered in the use of ML/DL for wastewater treatment (which required a critical analysis of the literature). These include issues with the quality and availability of data, issues with the complex relationships between data and with the dynamic and highly nonlinear behavior of the treatment processes that lead to an under-analysis of ML algorithms in the wastewater treatment processes optimizing domain, questions of model interpretability and transparency, integration problems with existing infrastructure, and atttitudinal challenges in the form of regulatory and operational acceptance.  
This systematic review, by PRISMA guidelines, employed a comprehensive search strategy across several databases.  
The evolution and interconnectedness of research in this field are revealed via bibliometric analysis, showing the progression from basic AI applications to advanced hybrid models; identification of key papers that have defined the trajectory of the field; unaffiliated research that distinguishes between monitoring, prediction, forecasting, control, and optimizing dimensions of the work; and apparent movement toward real-time applications and IoT integration.