To tackle the challenge of efficient flight path planning for unmanned aerial vehicle (UAV), an enhanced hybrid genetic-particle swarm algorithm (IHGPA) is proposed. This algorithm, based on particle swarm optimization (PSO), integrates multiple strategies to enhance both convergence performance and solution quality. Firstly, to improve global optimization, a partition optimization strategy is introduced into the IHGPA, and a dynamic parameter adjustment mechanism is employed to optimize the particle velocity and position update methods. Secondly, the genetic algorithm’s selection, crossover, and mutation operators are refined to further boost optimization capabilities. During selection, a combination of the roulette wheel method and simulated annealing algorithm is used to preserve elite individuals. In the crossover phase, probabilistic arithmetic crossover and an improved simulation binary crossover are integrated to increase population diversity. For mutation, Lévy flight long-step perturbation and polynomial mutation are fused to prevent premature convergence. Finally, by drviding the search area to exchange optimal solution in formation and implementing a convergence detection mechanism is implemented, where particles undergo secondary optimization if their fitness value falls below a predefined threshold, preventing the algorithm from getting trapped in local optima. Experimental results show that, in environment 1 with scattered obstacles, the best fitness value of the IHGPA undperforms genetic algorithm, particle swarm optimization, wolf pack algorithm, artificial bee colony algorithm, and dung beetle optimizer by 78.130%, 46.190%, 53.990%, 41.124%, and 67.376%, respectively. In environment 2, with dense obstacles, IHGPA′s best fitness value is reduced by 89.990%, 75.088%, 76.503%, 71.048%, and 81.061%, respectively. The IHGPA effectively generates safe, smooth, and optimal flight paths while demonstrating outstanding stability and reliability across multiple verification trials.