Corrosion is a significant cause of damage in industrial infrastructure, transportation systems, marine equipment, pipelines, and metal parts. Traditional methods for inspecting corrosion mainly rely on manual observation and regular maintenance. These processes are time-consuming, labor intensive, and are subjective, which can lead to human error. Delays in spotting corrosion can lead to serious structural failures, higher maintenance costs, operational downtime, and safety risks. To address these issues, this paper introduces a real-time AI-based Corrosion Detection and Monitoring System. This system uses the YOLOv5 deep learning framework along with a modern web-based structure. The new system combines computer vision, deep learning, and web technologies to automate the detection of corrosion and assess its severity. It uses the YOLOv5s object detection model to find corrosion areas in uploaded images and live camera feeds. A React.js frontend offers an engaging and responsive user interface. Meanwhile, a FastAPI backend handles image processing, runs the necessary calculations, and communicates results. The system evaluates detected corrosion areas using bounding box calculations to estimate the amount of corrosion and categorize its severity as mild, moderate, or severe. It also features graphical visualizations, historical tracking, and repair suggestions to support preventive maintenance. This framework provides nearly real-time detection with higher accuracy and less reliance on manual inspection. Its modular and scalable design allows it to be used in various industries, including maritime, civil infrastructure, manufacturing, automotive, and aviation. Tests show that the system successfully identifies corrosion under different environmental conditions while maintaining good computational performance. This solution represents a cost- effective and smart way to monitor structural health and perform predictive maintenance.