This article presents a smart water quality monitoring framework using an intelligent IoT wireless sensor network (WSN) system. Traditional water quality monitoring methods are outdated due to the need for real-time feedback and emergency responses. Wireless sensor networks (WSNs) are evolving to address monitoring, coverage, and energy management challenges. The integration of IoT in WSNs enhances real-time water monitoring. However, these systems struggle with accurate real-time data delivery, reconfigurability, and deployment in harsh environments. Electronic sensors are needed for effective IoT WSN integration, but few exist for water quality parameters. Artificial intelligence (AI) techniques are used to predict water quality indicators without electronic sensors by relating to available sensor data. This approach is still in its infancy and not yet standardized. The study focuses on predicting E. coli concentrations in water using machine learning (ML) algorithms. Six models—ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor—were tested on a dataset from four South African water treatment plants. The AdaBoost regressor performed best with an average mean absolute error (MAE) of 14.37 counts/100 mL, while stochastic gradient boosting performed worst with an MAE of 42.27 counts/100 mL. The best-performing parameter set (Set A) included pH, conductivity, chloride, turbidity, nitrates, and chlorophyll. The study highlights the challenges in water quality monitoring, including communication technology, power consumption, security, and sensor fabrication. The SWQM framework consists of a sensing system, communication system, and head end system (HES). The sensing system collects, processes, and transmits data, while the communication system relays data to the HES. The HES manages data acquisition, analysis, storage, and system control. The article discusses the use of AI in water quality monitoring, emphasizing the need for ML algorithms to predict water quality parameters when wireless sensors are unavailable. The study demonstrates the effectiveness of ML models in predicting E. coli concentrations, with the AdaBoost regressor showing the highest accuracy. The results indicate that using more commonly available wireless sensors (Set A) improves model performance, but adding Set B parameters can further enhance predictions. The study underscores the importance of addressing challenges in SWQM, including communication, power, security, and sensor reliability, to develop effective and efficient water monitoring systems.This article presents a smart water quality monitoring framework using an intelligent IoT wireless sensor network (WSN) system. Traditional water quality monitoring methods are outdated due to the need for real-time feedback and emergency responses. Wireless sensor networks (WSNs) are evolving to address monitoring, coverage, and energy management challenges. The integration of IoT in WSNs enhances real-time water monitoring. However, these systems struggle with accurate real-time data delivery, reconfigurability, and deployment in harsh environments. Electronic sensors are needed for effective IoT WSN integration, but few exist for water quality parameters. Artificial intelligence (AI) techniques are used to predict water quality indicators without electronic sensors by relating to available sensor data. This approach is still in its infancy and not yet standardized. The study focuses on predicting E. coli concentrations in water using machine learning (ML) algorithms. Six models—ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor—were tested on a dataset from four South African water treatment plants. The AdaBoost regressor performed best with an average mean absolute error (MAE) of 14.37 counts/100 mL, while stochastic gradient boosting performed worst with an MAE of 42.27 counts/100 mL. The best-performing parameter set (Set A) included pH, conductivity, chloride, turbidity, nitrates, and chlorophyll. The study highlights the challenges in water quality monitoring, including communication technology, power consumption, security, and sensor fabrication. The SWQM framework consists of a sensing system, communication system, and head end system (HES). The sensing system collects, processes, and transmits data, while the communication system relays data to the HES. The HES manages data acquisition, analysis, storage, and system control. The article discusses the use of AI in water quality monitoring, emphasizing the need for ML algorithms to predict water quality parameters when wireless sensors are unavailable. The study demonstrates the effectiveness of ML models in predicting E. coli concentrations, with the AdaBoost regressor showing the highest accuracy. The results indicate that using more commonly available wireless sensors (Set A) improves model performance, but adding Set B parameters can further enhance predictions. The study underscores the importance of addressing challenges in SWQM, including communication, power, security, and sensor reliability, to develop effective and efficient water monitoring systems.