Water quality models (WQMs) are indispensable tools for assessing, predicting, and managing water pollutioninrivers, lakes,reservoirs, and coastal waters. With increasing anthropogenic pressures, climate variability, and urbanization, reliable predictivemodelsare essential for sustainable water management. This paper presents a comprehensive review of WQMs, including their classification,modeling principles, applications, challenges, and emerging trends. Major models such as QUAL2K, WASP, SWAT, CE-QUAL-W2,MIKE11, HSPF, and AI-based approaches are evaluated in terms of their applicability to different water bodies, pollutant types,andspatial-temporal scales. Challenges including data scarcity, parameter uncertainty, computational demand, and integrationwithreal-timemonitoring systems are discussed. Future directions emphasize hybrid models, machine learning integration, IoT-enabledmonitoring,climate-adaptive modeling, and global-scale predictive frameworks. This study provides critical insights for researchers, policymakers,and water resource managers to implement data-driven, sustainable strategies for water quality management. Keywords: Water quality models; rivers; lakes; reservoirs; simulation; machine learning; sustainable management