Safety and mitigating potential damage. This study investigates an Internet of Things (IoT)- enabled framework designed for real-time fire monitoring and response, utilizing sophisticated deep learning methodologies. The paper examines the integration of intelligent sensors that continuously gather environmental data, encompassing temperature, smoke, and gas. Levels to promote early fire detection. The proposed system utilizes a deep learning model to accurately classify and predict fire incidents, significantly improving Response Time (RT)s. Furthermore, the architecture incorporates communication protocols that facilitate rapid data transmission to emergency response teams and urban management systems, ensuring timely intervention. Our approach underscores the significance of data fusion from many IoT devices, which enhances situational awareness and decision-making processes during fire emergencies. By addressing critical challenges such as scalability, interoperability, and the reduction of false alarms, this research provides a holistic solution for urban fire safety, ultimately advancing smarter and safer cities.