Traditional centralized traffic management systems are increasingly inadequate for handling the high-velocity data streams generated by fully autonomous and connected vehicle fleets. This paper proposes a decentralized "Swarm Intelligence" framework for real-time traffic orchestration in high-density urban corridors. By treating each autonomous vehicle (AV) as an intelligent agent capable of local decision-making and peer-to-peer (V2V) coordination, we eliminate the latency bottlenecks associated with cloud-based control. The study utilizes a bio-inspired ant-colony optimization (ACO) algorithm to manage intersection throughput and minimize "Stop-and-Go" waves without the need for traditional traffic signaling. Our methodology employs a high-fidelity microscopic traffic simulation to test the framework under varying levels of AV penetration. The results demonstrate that decentralized swarm coordination can reduce average travel time by 32% and carbon emissions by 14% through smoother velocity profiles. The findings provide a scalable architecture for future smart cities, where traffic flow is a self-organizing process rather than a centrally dictated command.