Structural Health Monitoring (SHM) is a powerful instrument used by engineers to analyze the actual condition of the structure under observation. The data that can be obtained with SHM systems, whether static or dynamic, are many and, as a result, are difficult to manage by hand. For this reason, researchers in recent years have increasingly begun to work with artificial intelligence (AI) for data processing and interpretation. AI attempts to replicate the logical flow of a human being but, unlike a human, it can process much more data and much faster. Various algorithms, such as Machine Learning (ML) algorithms and Transfer Learning (TL) algorithms have already been introduced in the processing of monitoring data.