This comprehensive review examines the use of Wireless Sensor Networks as a solution for addressing water quality monitoring and data scarcity. It compares Wireless Sensor Networks with traditional laboratory-based and in-situ monitoring methods, highlighting their superior respons e speed, cost-effectiveness, ease of deployment, and reliable measurements. The paper provides an overview of wireless sensor node architecture, discussing subsystems, Quality of Service requirements, and the significance of low power consumption in microcontroller units. Network solutions for short, medium, and long-range applications are explored, highlighting that Low-Power Wide Area Network is the most effective option for water quality monitoring. Furthermore, the review acknowledges the potential of machine learning techniques within Wireless Sensor Networks for Water Quality Monitoring, highlighting their versatility. A case study analysis of three LPWAN applications is presented, discussing their key characteristics, potential benefits, and important considerations for future implementations. By consolidating current knowledge, this review emphasizes the capacity of Wireless Sensor Networks to overcome data scarcity challenges in water quality monitoring. Valuable insights are provided for researchers, practitioners, and decision-makers seeking to leverage Wireless Sensor Networks, LPWAN technologies, and machine learning techniques for efficient and cost -effective global water quality monitoring.