<p>In the rapidly evolving landscape of the Internet of Vehicles, optimizing data trustworthiness and congestion control has emerged as a pivotal challenge. To address this challenge, this research proposes a novel approach that synergistically integrates the Adaptive Particle Convergence Optimization algorithm with blockchain-based architecture. The objective is to strike a balance between ensuring trustworthy data exchange and efficient communication within the Internet of Vehicles network. The proposed approach begins with a thorough investigation of the Internet of Vehicles intricacies, highlighting the critical need for optimized parameters to simultaneously enhance data trust and alleviate congestion. The proposed approach’s unique features, including dynamic convergence threshold adaptation, velocity scaling, and adaptive inertia weight, empower the algorithm to efficiently navigate the complex solution space. To validate the approach, a comprehensive simulation environment is established. Realistic traffic data is generated to emulate vehicular movement, while the Ethereum blockchain with Geth 1.10.4 client is employed to construct a private blockchain for data trust and security. The proposed model achieves a data trust level of 0.85, a congestion rate of 0.10, a communication overhead of 7.5&#xa0;ms, and a successful data sharing rate of 88% by effectively optimizing parameters and fostering equilibrium between data trustworthiness and congestion control. Comparative analyses against other state-of-the-art methods underscore its superiority across diverse performance metrics. This research not only contributes to the field of the Internet of Vehicle but also paves the way for efficient, secure, and trustworthy vehicular communication networks.</p>