Wrought iron has historically played a crucial role in industrial and infrastructural applications, and many heritage buildings still rely on these structures. However, corrosion poses a significant threat to their integrity, making early detection vital for effective maintenance and safety. Traditional inspection methods are often time-consuming, labor-intensive, and prone to human error, highlighting the need for more efficient monitoring solutions. This study introduces an innovative approach that integrates Building Information Modeling (BIM) with continuous structural assessment using Laser Scanner technology and Machine Learning algorithms. By combining these advanced tools, the proposed methodology enables real-time tracking of deterioration patterns, automating the identification of corroded areas with high accuracy. The Laser Scanner captures precise 3D representations of structural elements, while Machine Learning algorithms analyze the collected data to detect early signs of corrosion. The integration with BIM ensures a comprehensive digital record, facilitating predictive maintenance strategies and prolonging the lifespan of these historic infrastructures. This automated and data-driven framework not only enhances the efficiency of corrosion monitoring but also significantly improves the preservation efforts of wrought iron structures, ensuring their safety and sustainability for future generations.