The project will involve developing an advanced fire detection system using machine learning and computer vision. This system, therefore, captures video feeds from cameras with the assistance of OpenCV, making the video capture smooth. The video content is pre-processed and enhanced using Deep Convolutional Neural Networks (CNNs), which improve clarity and feature detection in difficult situations like smoke or low light. Integration of YOLOv5 object detection algorithm for fire detection ensures high precision and low false-positive rates by identifying fire incidents in real time at a detection threshold of thirty. In case of a fire detection, this system uses Pygame to initiate alert mechanisms and send multi-channel warning messages to guardians or other pertinent individuals. These notifications enhance fire safety and mitigation initiatives by ensuring prompt action is taken in real time. The algorithm proposed here is optimized for both speed and accuracy and, therefore, suitable for use in real-time application such as residential, commercial, and industrial surveillance. The project addresses critical needs of fire detection solutions in modern surveillance systems by providing enhanced fire detection through the integration of advanced video pre-processing, robust object detection, and reliable alert mechanisms.