With the increasing global demand for ultra-highspeed, low-latency communication, the development of 6G networks is inevitable. However, achieving seamless global coverage faces challenges due to complex geographical conditions and dynamic demand, making traditional terrestrial networks insufficient. Low Earth Orbit (LEO) satellites, with their low latency and high spectral efficiency, play a critical role in 6G networks. This paper focuses on LEO satellite constellation orbit design and coverage optimization, proposing a dynamic optimization strategy and AI-based framework using deep learning and reinforcement learning algorithms. First, a regional priority model is built using deep learning to identify high-demand areas based on global population distribution and terrestrial network characteristics. Then, deep reinforcement learning is applied to optimize orbit parameters, adjusting satellite deployment density to enhance coverage performance and resource utilization in priority areas.Simulation results show that this strategy significantly improves global coverage and resource efficiency, especially in high-population-density and remote areas, providing higher-quality communication services. This research offers innovative design ideas for global 6G coverage and provides a foundation for dynamic optimization and deployment strategies in future communication networks.