Structural Health Monitoring (SHM) has emerged as a powerful tool for condition-based preventive maintenance of civil infrastructure, enabling rapid, efficient, and non-invasive structural evaluation. Unlike traditional inspection methods, SHM systems reduce the need for direct human intervention in hazardous environments, enhancing both safety and operational efficiency. However, these systems often rely on siloed sensors, each capturing different physical quantities, and have only marginally adopted data fusion techniques to analyze and interpret the combined data effectively. In this context, the present study reports a laboratory-based investigation aimed at addressing this challenge through a controlled experimental campaign on a reinforced concrete (RC) frame subjected to a series of progressive damage scenarios. The monitoring setup combines various sensing technologies, including a UAV (unmanned aerial vehicle) for global displacement tracking, accelerometers for dynamic response measurement, strain gauges for localized stress monitoring, and a potentiometer for precise point displacement assessment. The diversity of these sensors offers a multi-physical and multi-scale view of the structure’s behavior under damage. A specific methodology was developed to integrate and cross-validate the data from the different sensors, ensuring consistency and robustness in the interpretation of structural changes. In this communication, special attention is devoted to the UAV-based displacement measurements, which provide a contactless and flexible alternative to traditional instrumentation. The results demonstrate the system’s effectiveness in identifying and characterizing damage, showing close agreement with the other sensing instruments. In particular, the UAV-based measurements show strong potential for detecting displacement patterns correlated with damage levels. These findings support the viability of UAV-assisted monitoring for post-event assessment, such as after earthquakes, and represent a promising step toward real-world implementation in structural monitoring strategies.