With the booming development of low altitude economy, UAVs are increasingly used in logistics and distribution, environmental monitoring, emergency rescue and other fields. For UAV path optimization, we propose a hybrid path planning strategy leveraging the Grey Wolf Optimizer (GWO) and Slime Mold Algorithm (SMA). The method improves the performance by introducing five innovative strategies: population initialization based on Logistic Chaos Mapping, which enhances the solution space traversal ability; logarithmic decay dynamically adjusts the control parameter a, which balances the ability of global exploration and local exploitation; optimizing the wolf position updating strategy by combining the SMA weighting mechanism, which dynamically allocates weights according to the fitness value; designing the slime mold perturbation term in conjunction with factor a, which improves the capacity to escape local optima and incorporating a probabilistic contraction mechanism to converge towards the global optimal position based on probability. Comparative experiments conducted in two distinct environments demonstrate that the enhanced grey wolf optimization algorithm outperforms alternative methods regarding optimization capability, convergence rate, and robustness.