Internet of Vehicle (IoV) networks face significant challenges due to their dynamic nature, including rapid topology changes, scalability issues, and efficient resource management. As the number of connected vehicles increases and the demand for real-time data processing grows, there is a need for advanced optimization techniques to enhance IoV performance. In this paper, a novel Bee-Inspired Clustered Internet of Vehicles Optimization (BICIO) framework is proposed to address these challenges. BICIO integrates advanced clustering techniques with bee colony-inspired algorithms to improve network efficiency and stability. The framework incorporates a hybrid clustering mechanism, adaptive cluster head selection, multi-level task allocation, and energy-aware load balancing. The framework shows strength in adapting to dynamic network conditions and efficiently managing resources in dense vehicular environments, making it suitable for both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. To evaluate the effectiveness of BICIO, an extensive simulation comparing is conducted with a number of state-of-the-art clustering techniques. Results demonstrate that BICIO outperforms existing solutions, improving overall network efficiency by up to 20% and reducing resource utilization imbalance by 30%. The BICIO framework demonstrates significant improvements in IoV network optimization, outperforming existing techniques in both performance and resource management.