The great complexity and rising population in urban environs necessitate putting together advanced systems that can successfully detect and identify missing individuals. A paper which brings out an AI-based framework that primarily uses facial recognition and deep learning methodologies to address the issue of locating missing persons has been explored. Thus, the system so provided should rely on real-time CCTV recordings combined with a whole facial recognition system. This system will contain CNNs, YOLO for capturing faces, ResNet50 for improving feature extraction, and KNN for fast comparison in real time against a dedicated database of missing persons. The system augments its scope by facilitating the secure submission of images and information pertaining to missing persons by community members, thus broadening the search capacity beyond conventional law enforcement assets. This engagement of the community enhances the prospects of prompt recovery and fosters improved cooperation between the public and governmental authorities. This framework offers a new approach toward enhancing public safety initiatives as well as shortening the response time to law enforcement through real-time processing, high accuracy, and scalability capabilities. The initiative tackles both technological and social dimensions of the search process, focusing on improved recovery rates, reduced search times, and therefore a safer community.