In recent years, vehicle theft has become a significant concern worldwide, necessitating the development of advanced stolen vehicle detection systems. This paper presents a novel approach for identifying and tracking stolen vehicles in real-time using a combination of IoT, AI-based image processing, and cloud computing. The proposed system integrates Automatic Number Plate Recognition (ANPR), GPS tracking, and a centralized database to cross-verify vehicle credentials. Utilizing deep learning techniques, the system enhances detection accuracy, reduces false positives, and enables swift action. The experimental results demonstrate the efficacy of the proposed method in improving law enforcement efficiency. The implementation of such a system can significantly reduce the workload on traffic authorities, automate stolen vehicle identification, and integrate with smart city frameworks for more comprehensive monitoring. Additionally, leveraging artificial intelligence for surveillance will enhance the adaptability of security measures against evolving theft techniques, making vehicle tracking more robust and efficient. Keywords: Stolen Vehicle Detection, ANPR, IoT, AI, Deep Learning, Law Enforcement, Traffic Surveillance