AI-driven solutions in wastewater treatment and agricultural reuse systems: A comprehensive review

Wastewater reuse for irrigation is a key solution to freshwater shortages and food crises caused by population growth and climate change, aligning with circular economy and sustainable development principles. However, traditional techniques struggle to handle the complex nonlinearities in wastewater reuse, which hinders broader adoption. Artificial intelligence (AI) can effectively capture and manage the complex dynamics in wastewater treatment and irrigation—two crucial stages in wastewater reuse—making it a promising technology for future development. This review aims to critically examine the application of AI in both wastewater treatment and agricultural reuse, highlighting technical limitations, regulatory misalignments, and implementation gaps. Studies show that AI enhances wastewater treatment by improving real-time water quality monitoring, effluent quality, energy savings, and cost reduction. AI also optimizes irrigation by improving water reuse efficiency, scheduling, and reducing risks like over-irrigation and soil contamination, boosting agricultural productivity and sustainability. However, existing AI models are primarily designed based on wastewater discharge standards, overlooking reclaimed water requirements. A notable example of this misalignment is the persistent focus of AI models on specific pollutants removal (e.g., nitrogen), aligned with traditional discharge standards, despite these pollutants no longer being key parameters under reclaimed water quality criteria (e.g., Regulation (EU) 2020/741). AI models also focus heavily on predicting conventional water quality parameters while neglecting contaminants of emerging concern (CECs). Moreover, many AI models are only validated in simulated environments and do not provide robust evidence of performance in real-world reuse systems. The review concludes with a roadmap of future research needs, offering practical insights for researchers, plant operators, and policymakers. This synthesis serves as a reference framework to guide the development of integrated, quality-aware AI systems for sustainable water reuse.