HydroSense 2.0: An IoT-Based Smart Water Quality Monitoring and Alert System with Machine Learning and Cloud Integration

Access to clean and safe water is a fundamental human right and a cornerstone of public health and environmental sustainability. In many regions, the absence of real-time, automated water quality surveillance leads to delayed identification of contaminants and public exposure to hazardous conditions. This paper presents the comprehensive development and deployment of HydroSense 2.0, an intelligent water quality monitoring system designed using Internet of Things (IoT) technologies integrated with cloud computing and machine learning. At its core, the system utilizes the ESP32 microcontroller interfaced with an array of industry-grade sensors measuring pH, temperature, turbidity, and Total Dissolved Solids (TDS). The captured data is transmitted to a cloud backend hosted on PythonAnywhere, where a Random Forest classification model processes the inputs to assess water potability. Real-time alerts are generated through SMS notifications and visualized on a web-based dashboard. The system was rigorously evaluated under laboratory and field conditions and is designed to operate in municipal pipelines, home filtration systems, and remote field applications. HydroSense 2.0 sets a precedent in scalable, low-cost, intelligent water quality monitoring by combining modular design, edge computing, and real-time analytics.