The advent of sixth-generation (6G) networks introduces unprecedented challenges in achieving seamless connectivity, ultra-low latency, and efficient resource management in highly dynamic environments. Although fifth-generation (5G) networks transformed mobile broadband and machine-type communications at massive scales, their properties of scaling, interference management, and latency remain a limitation in dense high mobility settings. To overcome these limitations, artificial intelligence (AI) and unmanned aerial vehicles (UAVs) have emerged as potential solutions to develop versatile, dynamic, and energy-efficient communication systems. The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning (CoRL) to manage an autonomous network. The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity, movement directions, allocate power, and resource distribution. Unlike conventional centralized or autonomous methods, CoRL involves joint state sharing and conflict-sensitive reward shaping, which ensures fair coverage, less interference, and enhanced adaptability in a dynamic urban environment. Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%, achieves convergence 40% faster, and reduces latency and energy consumption by 30% compared with centralized and decentralized baselines. Furthermore, the distributed nature of the algorithm ensures scalability and flexibility, making it well-suited for future large-scale 6G deployments. The results highlighted that AI-enabled UAV systems enhance connectivity, support ultra-reliable low-latency communications (URLLC), and improve 6G network efficiency. Future work will extend the framework with adaptive modulation, beamforming-aware positioning, and real-world testbed deployment.