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In recent years, a rise in missing person cases has posed challenges for law enforcement. This paper explores the various issues surrounding many unresolved cases and aims to uncover the contributing factors. Using a detailed analysis of police records, the study identifies patterns and challenges in resolving missing person cases. By understanding these dynamics, law enforcement agencies can refine strategies to enhance the likelihood of resolution, emphasizing the critical need for effective measures in addressing the growing issue of missing persons. Additionally, the paper proposes an innovative approach to locating missing persons using machine learning (ML) algorithms, specifically support vector machine (SVM) and K-nearest neighbors (KNN). Utilizing facial expressions as the basis for model training, the system swiftly and accurately identifies known individuals. The system, fed with a missing person dataset from Kaggle, outputs the person's identity based on features like gender, age and location. The results are then communicated to the police for further investigation. This streamlined approach enhances the efficiency of the search and identification process, contributing to more effective resolutions of missing person cases. The proposed system serves as a valuable tool for law enforcement in expediting investigations and addressing the critical issue of missing persons in a timely and efficient manner. [ABSTRACT FROM AUTHOR]