This study addresses the issue of insufficient solution accuracy in the standard Salp Swarm Algorithm for three-dimensional path planning of drones, proposing an improved algorithm that incorporates a chaotic Gaussian mutation learning mechanism. By enhancing the initial population generation method, leader update strategy, and worst individual optimization mechanism, the Tent chaotic mapping is employed to enrich population diversity, Gaussian mutation operation is introduced to enhance global search capability, and the learning strategy based on the refraction principle is utilized to strengthen local search performance. To verify the effectiveness of the algorithm, simulation experiments were conducted on six typical complex functions, the CEC2014 test function set, and three-dimensional path planning problems for drones. The experimental results demonstrate that the improved algorithm exhibits faster convergence speed and higher solution accuracy in function optimization. In path planning applications, the algorithm significantly improves path accuracy and convergence efficiency, effectively meeting the requirements of practical three-dimensional path planning.