The application of UAVs in surveillance, disaster management, and military operations has surged, necessitating robust and real-time tracking systems for moving targets. However, accurately tracking and predicting the trajectories of ground targets pose significant challenges due to factors such as target occlusion, varying speeds, and dynamic environments. To address these challenges and advance the capabilities of UAV-based tracking systems, a novel vision-based approach is introduced in this paper. This approach leverages the visual data captured by the UAV’s onboard cameras to achieve real-time tracking, geolocation, trajectory recovery, and predictive analysis of moving ground targets. By employing filter, regression and optimization techniques, the proposed system is capable of accurately estimating the target’s current position and predicting its future path even in complex scenarios. The core innovation of this research lies in the development of an integrated algorithm that combines object detection, target geolocation, and trajectory estimation into a single, cohesive framework. This algorithm not only facilitates the online recovery of the target’s motion trajectory but also enhances the UAV’s autonomy and decision-making capabilities. The proposed methods are validated through real flight experiments, demonstrating their effectiveness and feasibility.