This paper provides a comprehensive review of the current state of water quality monitoring (WQM) techniques, focusing on the integration of Internet of Things (IoT) wireless technologies and Machine Learning (ML) algorithms. The authors highlight the significance of continuous monitoring of water quality to ensure its usability and the importance of real-time data collection for efficient management. The paper discusses various IoT wireless technologies, including Low-Power Wide Area Networks (LPWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, and their applications in WQM. It also explores both supervised and unsupervised ML algorithms for analyzing and interpreting collected data, emphasizing their role in making accurate predictions and supporting decision-making processes. The review addresses the challenges and open research questions involved in integrating IoT and ML for WQM, including data acquisition, algorithm selection, and model evaluation. The paper concludes by highlighting the transformative potential of the integration of IoT and ML in advancing WQM across different sectors, such as wastewater treatment, household monitoring, and industrial settings.This paper provides a comprehensive review of the current state of water quality monitoring (WQM) techniques, focusing on the integration of Internet of Things (IoT) wireless technologies and Machine Learning (ML) algorithms. The authors highlight the significance of continuous monitoring of water quality to ensure its usability and the importance of real-time data collection for efficient management. The paper discusses various IoT wireless technologies, including Low-Power Wide Area Networks (LPWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, and their applications in WQM. It also explores both supervised and unsupervised ML algorithms for analyzing and interpreting collected data, emphasizing their role in making accurate predictions and supporting decision-making processes. The review addresses the challenges and open research questions involved in integrating IoT and ML for WQM, including data acquisition, algorithm selection, and model evaluation. The paper concludes by highlighting the transformative potential of the integration of IoT and ML in advancing WQM across different sectors, such as wastewater treatment, household monitoring, and industrial settings.