Artificial Intelligence for Sustainable Water Resource Management: Opportunities, Challenges, and Future Directions

Abstract

Global water resources face unprecedented stress from climate variability, population growth, and urbanisation, exposing the limitations of traditional, deterministic management approaches. Artificial intelligence has emerged as a disruptive paradigm for bridging the aforementioned operational gaps through advanced predictions, real-time monitoring, and system optimisation. This study conducted a qualitative systematic literature review of peer-reviewed research published between 2005 and 2026 to evaluate the integration of AI within sustainable water governance.

The literature synthesis of this study indicated that while machine learning and deep learning have enhanced short-term water demand forecasting and predictive maintenance, recent developments focus on regional water consumption modeling and AI applications for pollutant detection. However, challenges remain, including the opacity of AI models, geographic biases favouring developed regions, and a disconnect between AI outputs and institutional decision-making process.

This study concluded that the future maturity of digital water systems depends on a shift from purely technical performance to explainable AI, digital twins, and algorithmic auditing frameworks. Ultimately, AI can advance United Nations sixth Sustainable Development Goal only when it is seamlessly integrated  with collaborative, equitable governance and evidence-based policy structures.