Fire incidents pose a significant security threat that requires attention from all stakeholders. For example, Taipei reported 893 fire incidents in 2023 alone. This study aims to enhance security surveillance by promptly detecting fire incidents through the development of a patrol robot. The robot captures sensor signals from the MQ-5 and transmits them via MQTT to the Raspberry Pi control center, delivering real-time reports of smoke and fire to fire officials. Additionally, equipped with a camera, the robot streams footage using RTSP to Jetson for fire and smoke object recognition using the You Look Only Once (YOLO) algorithm. Our robot effectively detected smoke and fire using the MQ5-Sensor and transmitted the signals via MQTT. Furthermore, it analyzed the visual stream to identify fire and smoke patterns with YOLOv8, resulting in a significant increase in accuracy. The evaluation of our model yielded impressive performance metrics, including a precision of 93%, a recall of 97%, a precision-recall of 97.90%, and an F1-Score of 97%. In the future studies we will integrate a mini camera and Large Language Model (LLM) to enable the robot to communicate with humans to warn of early fire incidents.