Managing existing infrastructures represents a growing challenge for civil engineering, particularly regarding risk assessment and maintenance of bridges and viaducts. In recent years, innovative methods have been developed to improve early damage identification and optimise intervention strategies. This study proposes an innovative framework integrating Bridge Information Modeling (BrIM) with the Digital Twin (DT) paradigm, designed to provide concrete answers to critical infrastructure management and safeguarding issues. Adopting integrated remote sensing technologies, IoT sensors, and artificial intelligence algorithms allows not only to overcome the difficulties associated with the absence of structured data but also to revolutionise the inspection process and infrastructure lifecycle management, enabling predictive monitoring strategies, proactive maintenance and resource optimisation. The aim is to outline a scalable and operational approach, applicable on a large scale, transforming infrastructure management into a more efficient and automated data-driven system.