Reinforced concrete (RC) structures are fundamental to infrastructure and form the backbone of modern construction. However, these structures progressively degrade over time due to factors such as mechanical loading, material aging, environmental exposure, and steel reinforcement corrosion. Their assessment is therefore essential for ensuring structural safety, optimizing maintenance costs, and extending service life. Traditional inspection methods for RC structures are often destructive, time-consuming, subjective, and limited to localized areas. In contrast, Non-Destructive Testing (NDT) techniques can evaluate a wide range of structures quickly, at low cost, and with high resolution, without causing damage. Nevertheless, each NDT method has specific strengths and limitations, making integrated approaches particularly valuable. Recent studies demonstrate that combining NDT techniques can improve diagnostic accuracy, reduce uncertainties, and provide a more comprehensive and quantitative understanding of structural conditions. At this stage, the research proposes the integration of NDT approaches (GPR, ERT, SP, and UPV) to monitor damage, material properties, steel reinforcement type, and the overall condition of RC structures. For this purpose, laboratory experiments and field investigations on real structures have been conducted. In the laboratory, RC specimens have been evaluated using both direct and indirect testing methods. In the field, structural elements from a previously demolished bridge, including beams and columns recovered and transported to the laboratory, have been analyzed to enable comparison between newly prepared specimens and real components that were previously in service. However, multisensor geophysical methods generate large and complex datasets that require advanced processing and interpretation. In this context, Artificial Intelligence (AI), particularly machine learning and deep learning models, offers powerful tools to automatically detect, classify, and quantify structural damage, as well as to facilitate efficient data interpretation.