A decade of Artificial Intelligence in water management: A systematic review of progress, applications, and challenges (2010-2025)

Sustainable water resource management is an increasingly urgent global challenge, where conventional methods are often inadequate. Artificial Intelligence (AI) has emerged as a transformative technology offering advanced solutions. This paper presents a systematic review of the progress, applications, and challenges of AI in water management during the period 2010–2025. Through a thematic analysis of relevant literature, we identify three distinct evolutionary stages: an early stage dominated by traditional machine learning (2010–2015), a deep learning revolution (2016–2020), and an era of Advanced AI Integration and Innovation featuring hybrid models and physics-aware machine learning (2021–2025). Key findings show that AI excels in various applications, particularly high-accuracy water quality prediction, real-time monitoring systems, process optimization, and predictive analytics for disaster mitigation. Despite its significant strengths in accuracy and data processing, major challenges remain, including data availability limitations, lack of model interpretability (“black box”), and generalization difficulties. This review concludes that future research directions, such as Explainable AI (XAI) and domain knowledge integration, are crucial to overcoming these barriers and realizing the full potential of AI in creating intelligent, efficient, and resilient water management systems.