Ensuring access to safe water is vital for public health and environmental sustainability. However, conventional water quality monitoring methods are limited by high costs, lack of scalability, and delayed analysis. This survey explores recent innovations in IoT-based Water Quality Monitoring Systems (IoT-WQMS) integrated with Machine Learning (ML) and Deep Learning (DL) to enable real-time, automated water quality assessment. The review covers architectures utilizing multi-parameter sensors (e.g., pH, turbidity, TDS, DO, temperature), along with essential data processing techniques such as imputation and normalization. Advanced feature selection methods (RF-MOA, ensemble voting) and hyperparameter tuning techniques (QPSO, Grid Search) are discussed for model optimization. ML models like XGBoost, Random Forest, and ANN, as well as DL models such as CNN-LSTM and MS-CAGRU, demonstrate predictive accuracies up to 99.9%, supporting early contamination detection and regulatory compliance. Applications span urban rivers, aquaculture, and groundwater systems, offering actionable insights for efficient and sustainable water management. The paper also addresses key challenges including sensor calibration, data heterogeneity, and model adaptability, highlighting the role of hybrid AI and Explainable AI (XAI) in enhancing system robustness and transparency. This review provides a comprehensive perspective to guide future research and deployment of intelligent water monitoring solutions.