In complex environments, three-dimensional path planning for agricultural UAVs involves the comprehensive consideration of multiple factors, including obstacle avoidance, path optimization, and computational efficiency, which significantly complicates the achievement of safe and efficient flight. As environmental complexity increases, the search space expands exponentially, thereby making the problem more challenging to solve and categorizing it as an NP-hard problem. To obtain an optimal or near-optimal path within this vast search space, it is essential to balance the path length, safety, and computational cost. This paper proposes a novel UAV path planning method based on the Hybrid Multi-Strategy Dung Beetle Optimization Algorithm (HMSDBO), which effectively reduces path length and improves path smoothness. First, a new Latin hypercube sampling strategy is introduced to significantly enhance the population diversity and improve the global search capabilities. Furthermore, an innovative golden sine strategy is proposed to greatly enhance the algorithm’s robustness. Lastly, a new hybrid adaptive weighting strategy is employed to improve the algorithm’s stability and reliability. To validate the effectiveness of HMSDBO, this study compares its performance with that of the Adaptive Chaotic Gray Wolf Optimization Algorithm (ACGWO), Primitive Dung Beetle Optimization Algorithm (DBO), Whale Optimization Algorithm (WOA), Crayfish Optimization Algorithm (COA), and Hyper-Heuristic Whale Optimization Algorithm (HHWOA) in complex agricultural UAV environments. Experimental results show that the path lengths calculated by HMSDBO are reduced by 21.3%, 7.88%, 19.95%, 8.09%, and 4.2%, respectively, compared to the aforementioned algorithms. This reduction significantly enhances both the optimization effectiveness and the smoothness of three-dimensional path planning for agricultural UAVs.