The severe concerns regarding water scarcity and pollution necessitate the implementation of novel methods for monitoring and managing these challenges. This study investigates the utilisation of Internet of Things (IoT) in Water Quality Monitoring Systems (WQMS), with a specific emphasis on its fundamental elements, operational capabilities, and the incorporation of sensor technology and machine learning. The Internet of Things (IoT) enables the connection of equipment equipped with sensors to gather and analyse real-time data, hence improving the effectiveness of water quality monitoring. The IoT-WQMS functions by utilising modules for sensing, transmitting data, processing, and interfacing. Intelligent sensors oversee crucial water characteristics such as pH, temperature, turbidity, and Dissolved Oxygen (DO), with data analysed and presented through user interfaces. The development of sensor technology has resulted in the creation of small, real-time data-generating intelligent sensors, which are essential for IoT-WQMS. Precision and durability are crucial. Machine learning, specifically Artificial Neural Networks, aids in forecasting water quality parameters based on linked data, hence contributing to the development of a comprehensive Water Quality Index (WQI). Nevertheless, machine learning necessitates significant computational and large training data, both of which might be alleviated by utilising robust computing systems and dependable data sources. Conclusively, the utilisation of IoT in water quality monitoring offers a highly effective and immediate method for managing water resources. Advanced sensors and machine learning provide precise and practical information about water quality. This research establishes a foundation for future advancements in smart water monitoring and environmental monitoring technology. abstract environment.