The growing complexity of robotic applications in industrial and service domains has made path planning essential for ensuring the operational safety and efficiency of high-degree-of-freedom robotic arms. Path planning and obstacle avoidance are critical tasks in complex 3D environments, yet traditional algorithms often fail to provide optimal solutions, particularly in terms of path quality, computational efficiency, and adaptability to dynamic conditions. This study addresses these challenges by leveraging the RRT* algorithm, known for its asymptotic optimality, to enhance path planning for 6-DOF robotic arms. RRT* incrementally builds a search tree by sampling the configuration space, enabling efficient generation of collision-free paths in cluttered workspaces. An enhanced RRT* framework is introduced, incorporating cost-based pruning and informed sampling strategies to streamline the search process and improve trajectory optimization. This approach enables 6-DOF robotic arms to navigate complex environments efficiently and adapt to dynamic obstacles.