This study analyzes the application of reinforcement learning-based dynamic level design techniques in metaverse environments. The research examines key reinforcement learning algorithms including Q-learning, DQN, and PPO, focusing on their implementation in game level design through notable case studies: procedural content  eneration in No Man's Sky, the AI Director system in Left 4 Dead, and automatic level generation in Super Mario AI. The findings demonstrate that reinforcement learning-based level design produces significant positive effects on player immersion, replay value, and personalized gaming experiences. In metaverse environments specifically, the study confirms the feasibility of implementing dynamic environmental changes and difficulty adjustments based on user behavior patterns. When compared to traditional level design approaches, reinforcement learning-based methods show strengths in generating user-customized content that adapts to individual player preferences and skill levels. The research contributes to the field by establishing a framework for understanding how reinforcement learning can enhance level design in both games and metaverse platforms, while proposing future directions for AI-based game development.