<p>To deal with the problem of safety-enhanced unmanned aerial vehicle (UAV) path planning in complex environments that present multiple threats, including collisions, loss of control, and yaw instability. This study proposes an enhanced artificial jellyfish search algorithm, named SMSCJS, which incorporates spherical vectors and a multi-strategy collaborative approach. First, a cost function is established to transform the path planning problem into an optimization task, ensuring compliance with safety constraints. The SMSCJS algorithm integrates Lévy flights and cosine-decreasing inertia weights, which accelerate convergence and prevent the algorithm from becoming trapped in local optima. Additionally, the fusion flood algorithm (FLA) strategy enhances local exploitation and path search efficiency. Next, spherical vectors are utilized to explore the UAV’s configuration space by correlating the jellyfish’s position with the UAV’s speed, turn angle, and climb/dive angle. The generated trajectories are subsequently refined using cubic B-spline curves to ensure the flight paths are suitable for UAV operations. Finally, to evaluate the performance of SMSCJS, four benchmark scenarios based on real digital elevation model (DEM) maps are developed. The results show that SMSCJS outperforms the traditional jellyfish search (JS), spherical vector-based JS (SJS), and multi-strategy collaborative JS (MSCJS). In many cases, the proposed algorithm also surpasses other prominent meta-heuristic optimization algorithms, including particle swarm optimization (PSO), simulated annealing (SA), artificial bee colony (ABC), hiking optimization algorithm (HOA) and flood algorithm (FLA). These findings validate the effectiveness of SMSCJS for safety-enhanced UAV path planning.</p>