The rapid adoption of AI is transforming industrial wastewater treatment by enabling real-time monitoring, predictive control, and operational optimization. Publications on the subject accelerated sharply between 2020 and 2024 (nearly doubling), reflecting the expansion of research and practical applications. This review maps global research trends (2014–2024), identifies the most widely used AI techniques and application domains, and evaluates benefits, limitations, and priorities for future deployment. A bibliometric and qualitative analysis of Web of Science records was carried out using VOSviewer and CiteSpace, with performance metrics extracted from exemplary case studies. Deep learning and machine learning approaches predominate, with numerous case studies reporting very high predictive performance (R² typically between 0.90 and 0.999; some studies ≈achieving an R² of approximately 0.9997). Documented operational gains include energy reductions of up to ~ 20% and membrane separation efficiencies approaching 99%. Market projections (CAGR ≈ 6.53% through 2030) indicate continued investment in AI for wastewater treatment systems. The primary barriers are data quality and heterogeneity, limited model interpretability, high upfront costs, and the energy-intensive nature of data-intensive solutions. To accelerate responsible adoption, we recommend creating standardized and shareable datasets and benchmarks, developing interpretable physics-based hybrid models, implementing energy-efficient and edge computing solutions, conducting pilot projects with techno-economic and life-cycle assessments, and establishing targeted training programs and regulatory guidelines.
Filho et al. (2026) studied this question.