Reliable assessment of aging infrastructure poses a significant challenge in civil engineering, particularly in detecting corrosion within reinforced concrete structures. This study introduces a novel approach that integrates Unmanned Aerial Vehicles, artificial intelligence, and a custom-designed flying robot to conduct non-destructive testing (NDT) for corrosion detection. A pilot project was conducted on a box-girder bridge in Switzerland, where half-cell potential (HCP) measurements and high-resolution images were gathered to create a digital twin of the structure. The flying robot, equipped with a specialized sensor, facilitated precise electrochemical data collection in hard-to-reach areas, effectively overcoming GPS limitations. AI-driven analysis was utilized for automated damage detection, while photogrammetry techniques were applied for 3D reconstruction and spatial referencing of NDT data with deviations of a few centimeters. The results demonstrate the feasibility of employing flying robots for accurate corrosion risk assessment without disrupting traffic. The HCP and resistivity values indicate a low probability of corrosion in the examined area. This approach underscores the potential of automated, data-driven inspections for enhanced infrastructure maintenance. Future efforts will concentrate on real-time adaptive inspection strategies and multi-sensor integration to improve data acquisition efficiency and accuracy.