The practice of artificial intelligence (AI) in aquaculture water management represents a transformative shift toward optimizing the productivity and sustainability of aquaculture systems. The present study explores the role of AI technologies in monitoring, analyzing, and managing water quality in various aquaculture setups, including recirculating aquaculture systems (RAS), constructed wetlands in recirculating aquaculture systems and aquaponics in aquaculture systems. AI-driven solutions, such as machine learning algorithms, predictive analytics, and real-time monitoring, have the potential to significantly improve water quality management by enabling precise control of key factors such as temperature, dissolved oxygen, pH, and nutrient levels. By leveraging AI, aquaculture operations can reduce resource consumption, minimize environmental impacts, and enhance the productivity and health of aquatic species. This review also discusses the current advancements in AI applications for early detection of water quality issues, decision-making support systems, and automated responses to environmental changes. The integration of AI in aquaculture water management offers promising avenues for sustainable development in the sector, ensuring both economic viability and environmental stewardship.