As intelligent transportation systems have quickly expanded, security vulnerabilities in conventional vehicle verification mechanisms have struggled to keep pace with increasingly sophisticated falsification attacks, illegal accesses, and identity fraud. Existing systems are dependent on easily forged physical documentation and poor single factor authentication, leading to major security vulnerabilities that endanger the integrity of smart transportation networks. This research tackles these issues by proposing a cutting-edge AI-powered multi factor vehicle verification framework that innovatively fuses biometrics security, cryptographic validation, and real-time threat detection. With RSA-2048 encrypted QR codes and X.509 digital certificates, our solution combines deep learning facial recognition, which produces 128-dimensional feature vectors and matches them to Euclidean distance (matching accuracy 96.5–98.2% for various states) and presents industrial-grade secured goods, which nobody can tamper with. We built the system architecture on Flask with MySQL back-end support that is capable of scaling up while not compromising on authentication time (average is 0.5 s in our case). Our AI anomaly detection framework uses both supervised learning and unsupervised learning models to detect fraudulent patterns with 94.5% accuracy and only 1.9% false positive. With a 98.3% success rate against advanced attacks like deepfakes and common spoofing methods used in RFID and License Plate Recognition systems, our evaluation shows the strong security of the proposed model. This work provides a complete and practical security framework for future smart transportation systems, making it both secure and easy to deploy in busy environments.