Navigating harsh environments requires innovative approaches to path planning to ensure robustness and accuracy amidst complex obstacles and demanding conditions. This study presents a hybrid algorithm combining the Whale Optimization Algorithm (WOA) and Dynamic Multi-Swarm Global Particle Swarm Optimization (DMSGPSO) to address these challenges effectively. The hybrid algorithm leverages WOA’s wide exploration capabilities, inspired by humpback whales’ bubble-net hunting behavior, and DMSGPSO’s adaptive balancing of global and local search. This integration enhances the algorithm’s ability to avoid local optima and achieve precise path optimization. Experimental results on a \(700 \times 700\) grid environment with various static obstacles demonstrate that the proposed method reduces path cost by 2.11% compared to standard WOA and by 3.42% compared to PSO. Furthermore, the hybrid algorithm converges 11.16% faster than WOA and 33.38% faster than PSO, achieving its optimal path within 8 iterations. These results underline the algorithm’s suitability for nonholonomic robotic navigation in static environments. Its potential extends to real-time robotic deployments such as search and rescue missions, autonomous delivery in industrial sites, and exploration tasks in disaster-stricken zones where adaptability and efficiency are critical. Thus, the study sets a new standard for efficient and reliable path planning in complex environments.