This paper presents an IoT-based automatic water quality monitoring system with an optimized neural network for real-time water quality prediction. The system uses multiple sensors to measure physical and chemical parameters of water, including temperature, pH, turbidity, and TDS. Sensor data is transmitted to a cloud database via a WiFi setup, and an Arduino-based core controller processes the data. An optimized neural network, enhanced by a hybrid GA-PSO algorithm, is used to predict water quality with high accuracy. The system allows for remote monitoring and analysis of water quality, ensuring the safety of drinking water. The proposed system outperforms traditional methods, achieving 91% accuracy in water quality prediction. The system is designed to continuously monitor water quality, enabling early detection of contamination and ensuring the safety of water sources. The system is implemented using an Arduino model, ESP8266 Wi-Fi module, and various sensors. The performance of the system is evaluated using a dataset of 3,276 water samples, and the results show significant improvements in accuracy, precision, and recall. The system is suitable for real-time water quality monitoring and can be applied to various water sources, including groundwater, surface water, and industrial water. The proposed system is cost-effective, easy to use, and suitable for large-scale deployment. The system is designed to provide reliable and accurate water quality data, ensuring the safety of drinking water for millions of people.This paper presents an IoT-based automatic water quality monitoring system with an optimized neural network for real-time water quality prediction. The system uses multiple sensors to measure physical and chemical parameters of water, including temperature, pH, turbidity, and TDS. Sensor data is transmitted to a cloud database via a WiFi setup, and an Arduino-based core controller processes the data. An optimized neural network, enhanced by a hybrid GA-PSO algorithm, is used to predict water quality with high accuracy. The system allows for remote monitoring and analysis of water quality, ensuring the safety of drinking water. The proposed system outperforms traditional methods, achieving 91% accuracy in water quality prediction. The system is designed to continuously monitor water quality, enabling early detection of contamination and ensuring the safety of water sources. The system is implemented using an Arduino model, ESP8266 Wi-Fi module, and various sensors. The performance of the system is evaluated using a dataset of 3,276 water samples, and the results show significant improvements in accuracy, precision, and recall. The system is suitable for real-time water quality monitoring and can be applied to various water sources, including groundwater, surface water, and industrial water. The proposed system is cost-effective, easy to use, and suitable for large-scale deployment. The system is designed to provide reliable and accurate water quality data, ensuring the safety of drinking water for millions of people.