To overcome the inherent limitations of the conventional Spider Monkey Optimization (SMO) algorithm in UAV trajectory planning, which include protracted search durations, extensive ranges, susceptibility to local optima, and suboptimal solution acquisition, we propose an Enhanced Spider Monkey Optimization (ISMO) algorithm.Initially, the ISMO algorithm integrates real-world environmental parameters, including three-dimensional terrain and UAV-specific constraints.Subsequently, the algorithm introduces a co-evolutionary strategy that segments the trajectory into multiple sub-paths, optimizing each segment independently.This segmentation is achieved by further subdividing the population associated with each sub-path into sub-populations, ensuring sustained communication among them. This methodology diversifies the search vectors of the individuals within the population, thereby enhancing the algorithm’s capacity to circumvent local optima and bolstering path fidelity.Furthermore, the convergence rate is significantly enhanced through a meticulously crafted search operator. Ultimately, the ISMO algorithm undergoes rigorous testing in scenarios involving extensive, long-distance UAV flights. Empirical results substantiate that ISMO surpasses various other advanced algorithms, notably the Improved Fish Swarm Algorithm (IMAFSA), Improved Particle Swarm Optimization (IPSO), Improved Ant Colony Algorithm (IACA), Moth Flame Optimization Algorithm (MFO), and the original Spider Monkey Optimization Algorithm (SMO). Specifically, ISMO demonstrates an accelerated search velocity, an enhanced optimal range, and superior path fidelity compared to the IMAFSA, in addition to a heightened overall success rate in optimization endeavors.