To address the issues of insufficient initial population diversity, weak global optimization capability in the early stages, low convergence accuracy in the later stages, and susceptibility to local optima in the snake optimizer (SO) for solving robotic path planning problems, a multi-strategy enhanced snake optimizer (MSESO) is proposed. The MSESO utilizes a good point set method to initialize the snake population, thereby increasing the diversity of the initial population and ensuring more comprehensive coverage of the search space. It introduces two oscillation factors to balance the process of global search and local exploitation, dynamically updating the search range. Additionally, the integration of an adaptive elite opposition-based learning strategy effectively leverages valuable information from the population to improve population quality, enhancing the likelihood of approaching the optimal solution, accelerating the algorithm′s convergence speed, and improving convergence accuracy. The MSESO is applied to robotic path planning, beginning with ablation experiment to verify the effectiveness of the proposed strategies. Subsequently, comparative experiments on maps of varying complexity are conducted to assess the pathfinding performance of MSESO against other algorithms, demonstrating the superiority of the improved algorithm. Ablation experiment results show that the proposed strategies in MSESO significantly enhance path planning performance. Comparative experiment results indicate that MSESO outperforms the control algorithms in terms of average path length, path length variance, and average number of iterations, validating the robustness and superiority of MSESO in path planning.