High-quality global path planning is one of the key technologies enabling autonomous navigation of unmanned surface vehicles (USVs). To address the global path planning problem for USVs in complex obstacle environments, this paper proposes a global path planning method based on the multi-strategy enhanced slime mould algorithm (ME-SMA). To overcome SMA’s limitations such as uneven initial population distribution, slow convergence speed, and proneness to local optima, ME-SMA employs several enhancements: it optimizes population initialization using improved Logistic chaotic mapping to enhance global exploration; incorporates crossover, mutation, and selection strategies from genetic algorithms to improve local exploitation efficiency; and introduces the golden sine strategy to dynamically adjust the search direction, thereby avoiding premature convergence. To validate the effectiveness of ME-SMA, we tested it on nine types of benchmark functions. The results show that ME-SMA achieves superior convergence accuracy and stability compared to the original SMA and other comparative algorithms. Simulation experiments in identical complex obstacle environments further demonstrate that ME-SMA significantly improves convergence speed, task completion time, and navigation distance. Compared to the other experimental algorithms, ME-SMA achieves an average reduction of 1.8% in path length and an average improvement of 28.22% in stability, highlighting its high efficiency and practical engineering value for USV global path planning applications.