Unmanned Aerial Vehicles (UAVs) have emerged as ideal tools for aerial surveillance and task point access due to their high maneuverability, cost-effectiveness, and ease of deployment. However, their application in large-scale missions is limited by battery capacity. To overcome this limitation, this study explores a solution that uses ground vehicles traveling along road networks to provide charging support for UAVs, extending their operational range. The goal is to determine the optimal paths for both UAVs and ground vehicles in a highly coupled manner. A hierarchical adaptive hybrid path planning algorithm is proposed to solve this problem. The first stage employs a greedy weighted-sum approach to optimize the ground vehicle’s path. In the second stage, UAV task paths are planned based on the optimal ground vehicle path, integrating heuristic and metaheuristic techniques to satisfy energy constraints and time window requirements. The effectiveness and efficiency of the proposed algorithm were validated through comparisons with a mixed-integer linear programming (MILP) method that provides exact solutions. The algorithm achieved near-optimal results, deviating by only 5%–10% from the baseline while delivering computational speeds 6 to 30 times faster. Furthermore, the study examined the impact of road network complexity, UAV fleet size, and task point distribution on algorithm performance, offering practical guidance on balancing task completion time and energy consumption in real-world applications.