Efficient mission planning, including task allocation and path planning, is crucial for the successful operation of unmanned aerial vehicles (UAVs) and connected autonomous vehicles (CAVs) in complex scenarios. This article introduces an innovative mission planning approach that employs a collaborative model combining graph neural networks (GNNs) and Transformers to meet the intricate requirements of coordinating UAVs and CAVs. Our model excels in dynamic task allocation and accurate path planning, thereby boosting operational efficiency and reducing computational demands. We outline the shortcomings of current methods, notably their limited adaptability to dynamic changes and their substantial computational costs. By utilizing GNNs to capture complex interrelations and Transformers for effective information processing, our approach achieves greater adaptability and scalability. Experimental results demonstrate that our model surpasses leading methods, showing a 12% improvement in task allocation accuracy for UAVs and 10% for CAVs. Furthermore, we assess the model’s performance under various conditions, confirming its robustness and adaptability. This research provides a holistic solution for mission planning in UAV and CAV systems, setting the stage for future enhancements in autonomous vehicle coordination across logistics, surveillance, and disaster management sectors.