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Smart City initiatives are increasingly utilizing the IOT and WSN to optimize the management of essential resources like water. Due to growing pollution caused by industrial development, urban expansion, and the use of agricultural chemicals, there is a pressing need for dependable water quality monitoring solutions. The proposed system leverages IoT and WSN technologies to deliver real-time data, enabling authorities to efficiently oversee and protect water resources. Unlike traditional systems, which calculate on short- range, power-empty wireless technologies, this system makes use of Wi- Fi, which is both affordable and energy-effective. The system uses detectors, a microcontroller, and a Wi- Fi module to transmit data about water quality to an IoT platform for analysis and visualization. It aims to prognosticate parameters similar as TDS, turbidity, temperature, and conductivity. This design also presents a deep knowledge- rested result for microorganism type in water microscope images, which employs the VGG16 convolutional neural network (CNN) armature. The system is designed to help experimenters, water quality operation armies, and public health officers estimate water safety by relating specific microorganisms similar as Amoeba, Euglena, Hydra, Paramecium, Rod Bacteria, globular Bacteria, helical Bacteria, and incitement. Artificial intelligence algorithms use detector data to prognosticate complaint trouble, potentially saving lives. This result addresses the global challenge of water impurity, in which over 2 billion people calculate on unsafe water sources, by furnishing a hastily, more effective system for testing water quality and prognosticating complaint.