ABSTRACT. Fire incidents continue to pose serious risks to human life, critical infrastructure, and the global economy, especially in densely populated urban and industrial environments. Conventional fire detection systems often experience high false alarm rates, latency issues, and insufficient resilience against cyber vulnerabilities, making them unreliable for modern safety applications. To overcome these limitations, this study presents a secure IoT-enabled multi-sensor framework that integrates temperature, smoke, flame, and gas sensors through an ESP32 microcontroller. Sensor data are transmitted using MQTT over TLS with AES encryption to ensure confidentiality and integrity during communication. The proposed framework employs cloud-based analytics for multi-sensor data fusion, anomaly detection, and event validation, thereby enhancing system accuracy and reducing false alarms by up to 73%. The inclusion of AWS IoT Core and IoT Analytics facilitates intelligent decision-making, low-latency alerting, and adaptive data processing. Moreover, by embedding cybersecurity mechanisms such as encrypted channels and secure cloud computing, the system resists data tampering, spoofing, and denial-of-service attacks. Experimental evaluations conducted under controlled conditions demonstrate that the proposed model achieves a 96% detection accuracy, 92% security success rate, and an average response time of 2 seconds, outperforming conventional single-sensor systems. The framework’s scalable and modular design makes it suitable for deployment in smart homes, industrial facilities, and smart cities, providing a cyber-resilient foundation for next-generation fire safety management.