Structural Health Monitoring (SHM) has become a crucial activity in civil engineering to enhance the performance of aging infrastructure. With the unprecedented evolution from Industry 3.0 and the world of next-generation sensing technologies, the beginning of the fourth industrial revolution (Industry 4.0) has brought forth recent advancements such as Internet-of-Things (IoT), Big Data analytics, cloud computing, and cybersecurity to automate and leverage SHM methods over traditional inspections. However, connecting these technologies and establishing a comprehensive and autonomous digital framework for SHM applications have been a challenge. Presenting real-time processed SHM data in a live digital interface faces various hurdles, including data transfer delays and the need for manual processing using offline tools. This study, hence, explores emerging building information modeling (BIM) and IoT via an Arduino microprocessing unit to track and visualize data from time and frequency domains in real time. It aims to enable continuous data monitoring, real-time data processing, and storing data in a web-based database rather than relying on offline resources that require manual intervention. The data subsequently moves from the IoT device into a structured online database into BIM with the help of the latter’s visual scripting interface. The proposed real-time SHM method is experimentally validated using a laboratory application: a randomly excited three-story model under different health conditions. Generative warning dispatches are visualized via automated threshold monitoring of both time and frequency domains under normal healthy conditions. The research thus exhibits static and dynamic data in a comprehensive BIM database while ensuring an automated and live workflow that empowers decision-making.