This paper presents an innovative solution to the issue of infrastructure deterioration in the U.S., where a significant portion of facilities are in poor condition, and over 130,000 steel bridges have exceeded their lifespan. Aging steel structures face corrosion and hidden defects, posing major safety risks. The Silver Bridge collapse, resulting from an undetected flaw, highlights the limitations of manual inspection methods, which often miss subtle or concealed defects. Addressing the need for improved inspection technology, this work introduces an AI-powered magnetic inspection robot. Equipped with magnetic wheels, the robot adheres to and navigates complex ferromagnetic surfaces, including challenging areas like vertical inclines and internal corners, enabling thorough, large-scale inspections. Utilizing MobileNetV2, a deep learning model trained on steel surface defects, the system achieved an 85% precision rate across six defect types. This AI-driven inspection process enhances accuracy and reliability, outperforming traditional methods in defect detection and efficiency. The findings suggest that combining robotic mobility with AI-based image analysis offers a scalable, automated approach to infrastructure inspection, reducing human labor while improving detection precision and the safety of critical assets.