<![CDATA[Fire poses a severe threat, leading to significant loss of life, property, and environmental damage, underscoring the critical need for early detection and rapid response. This study proposes and implements an Internet of Things (IoT)-based fire early warning system utilizing an ESP32 microcontroller, integrated with multiple sensors, and Telegram as a real-time communication platform. The system continuously monitors environmental conditions through a DHT22 temperature sensor, an MQ-7 carbon monoxide (CO) gas sensor, and a flame sensor. A rule-based approach is employed to classify conditions as either normal or fire events, based on predefined threshold values, specifically temperature greater than or equal to 40 degrees Celsius and CO concentration greater than or equal to 200 ppm. To ensure comprehensive alerting, the system incorporates a dual-layer warning mechanism, providing local alerts via a buzzer and remote notifications through Telegram. An interactive Telegram bot interface is also implemented, facilitating real-time monitoring and multi-user notification management. Performance evaluation, conducted using a confusion matrix with 300 testing samples consisting of 150 normal and 150 fire conditions, demonstrated high classification efficacy. The system achieved an accuracy of 92.6 percent, a precision of 93.2 percent, a recall of 92.0 percent, and an F1-score of 92.6 percent. Furthermore, the system exhibited excellent responsiveness, with an average notification delay of 3.2 seconds, indicating near real-time performance. This integration of multi-sensor detection and Telegram-based communication significantly enhances the reliability and accessibility of fire alerts, offering an effective, low-cost, and scalable solution suitable for various early fire warning applications.]]>
