<p>The socio-cultural demography in Nigeria, especially in urban slums, presents communities with limited access to safe water, increasing the risk of disease outbreaks and poses major threat to public health. Traditionally, groundwater quality monitoring methods often involve on-site sample collection and transmission to designated laboratories for analysis. This can be time-consuming, expensive, and impractical in remote or hard-to-access settlements. This study developed a new methodological approach for real-time remote water quality monitoring consisting of a decentralized Internet of Things (IoT) and Citizen Science Framework. The system employed off-the-shelf water quality sensors, a wireless sensor network, and the TUYA platform to facilitate real-time data acquisition monitoring and analysis. Precisely, the following parameters namely: Temperature, Electrical conductivity, pH, Salinity, Specific gravity and Total Dissolved Solids were measured. A machine-learning-based soft-sensor and inference engine validated sensor readings and predicted water potability. Local high school students, trained as citizen scientists, ensured sensor safety and data integrity. Contamination events sent via digital channels enabled timely interventions, offering cost-effective solutions.</p>