Path planning and collision avoidance are among the core challenges that mobile robots must overcome while navigating dynamic environments. In this study, we propose a new comprehensive approach, Quad_D*_Fuzzy, which integrates Quadtree decomposition, D* Lite algorithm and Fuzzy Logic to address these challenges. The Quadtree method is employed for environment decomposition, enabling efficient collision avoidance and path planning with D* Lite. Fuzzy Logic is then utilized to generate real-time decisions, dynamically adapting the robot to its surroundings during navigation. The outcomes of simulations in varied scenarios–dense, room, and trap, which emulate real-life situations–were used to assess the proposed solution. The numerical results show that our solution demonstrates a 100% success rate, hence the best compared to previous methods regarding success rate weighted path length and smoothness. Moreover, with up to 80% reduction in planning time for dynamic settings, our approach is promising for practical deployment within real-world environments.