Gas leakage is a critical safety issue in residential, commercial, and industrial environments, often leading to accidents such as fire, explosions, and health hazards due to toxic gas exposure. Early detection and quick response are essential to reduce risks and ensure safety. This project presents an AI-driven IoT-based gas leakage prediction and automated emergency response system integrated with cloud analytics for efficient monitoring and control. The system uses gas sensors to continuously detect the presence and concentration of harmful gases. The collected data is processed using a microcontroller and transmitted to a cloud platform for storage and analysis. Artificial Intelligence techniques are applied to analyze both real-time and historical data, enabling the system to predict possible gas leakage conditions before they become dangerous. In addition to detection and prediction, the system includes an automated emergency response mechanism. When a gas leak is detected or predicted, the system immediately activates alarms and initiates safety actions to minimize potential hazards. It reduces the human intervention. The IoT integration allows users to monitor the system remotely and receive alerts through cloud-based applications. The cloud platform also maintains historical data for performance analysis and system improvement. The proposed system is cost-effective, reliable, and easy to implement, making it suitable for various real-world applications. Overall, the project enhances safety by combining IoT, AI, and cloud technologies into a smart gas leakage detection and prevention system.

Keywords:  Gas Leakage Detection, Internet of Things (IoT), Artificial Intelligence (AI), Cloud Analytics, Gas Sensors, Embedded Systems, Automation, Safety Monitoring, Predictive Analysis, Emergency Response System.