Missing person cases are a serious global issue that require fast and accurate identification systems. Traditional methods rely on manual investigation, which is time-consuming and often inefficient. This paper presents an AI-based Missing Person Tracking System that uses face recognition techniques based on computer vision and deep learning [1].The system uses Convolutional Neural Networks (CNNs) to extract facial features and generate embeddings for accurate identification under different conditions such as lighting and facial expressions. OpenCV is used for face detection and preprocessing, [2] and the extracted features are converted into numerical vectors. These vectors are compared with a centralized database using similarity measures like Euclidean distance to find matches.The system also provides a user-friendly interface that allows users and authorities to upload images and retrieve results in real time. This approach improves automation, reduces manual effort, and increases search efficiency. Overall, the system offers a reliable and scalable solution for identifying missing persons and supporting public safety.