The current landscape of data-centric Internet of Vehicles (IoVs) encompasses a fusion of Human-driven Vehicles, Autonomous Vehicles, Road-Side Units, and edge-based devices engaged in periodic communication. Given the stringent latency requirements inherent in vehicular communications, the emergence of edge-based vehicular Digital Twins (DTs) plays a pivotal role in problem-solving, ensuring rapid response, regulatory compliance, and seamless availability. While these communications serve as the backbone of IoV, they also create an opportune environment for cybercriminals to exploit. Vulnerabilities at the network layer facilitate intrusions, resulting in a surge of data falsification attacks in recent years. Addressing this challenge demands resilient and intelligent threat detection schemes capable of adapting to the dynamic nature of IoV. This study conducts a comprehensive examination of the vulnerabilities in Vehicle-to-DT (V2DT) data communication through the lens of an attacker utilizing False Data Injection Attack (FDIA). It utilizes cutting-edge Blockchain-based decentralized storage and buffering mechanisms for vehicle dynamics data en route to edge-based DTs. Further, deep learning-powered sensor data analysis serves as an additional layer of security. Evaluation of the proposed threat detection and mitigation model demonstrates 100% tamper detection in V2DT communication, coupled with a 96% accurate classification of anomalous driving behaviors, including aggressive driving or FDIAs.