A Bird’s-Eye View of Coagulation–Flocculation Integrated Artificial Intelligence in Wastewater Treatment: Research Trend, Challenges and Future Prospects

The integration of artificial intelligence (AI) with coagulation–flocculation (C-F) processes represents a significant advancement for optimizing wastewater treatment. However, a comprehensive analysis of the research landscape, trends and collaborative networks in this interdisciplinary field remains lacking. This study addresses this gap by conducting a bibliometric analysis of 251 Scopus-indexed publications (2000–2025) using VOSviewer and Bibliometrix. The objective was to map the intellectual structure, quantify growth trends and identify key research themes and contributors. Results indicate a surge in publications post-2015, with environmental science and engineering as dominant subject areas. China, Iran and Nigeria are leading contributors though geographical concentration suggests a need for broader collaboration. Keyword analysis reveals a thematic evolution from basic artificial neural networks towards advanced machine learning and deep learning, primarily focused on optimizing coagulant dosage and predictive control. Despite promising advancements, challenges related to data dependency, model interpretability and infrastructure integration persist. Future prospects hinge on developing explainable and hybrid AI models, leveraging the Internet of Things for real-time adaptation and fostering interdisciplinary research to bridge the gap between data science and process engineering. This analysis provides a foundational overview to guide future research towards more robust, transparent and widely applicable AI-driven C-F systems in sustainable wastewater management.