In the last decades, it has become increasingly important to monitor structures and infrastructures to determine their behaviour during normal service life and during particular stressful events such as earthquakes. In this way, it is possible to define the state of health of the building, whether it is healthy or damaged, the level of damage, and its eventual evolution. At the same time, it is possible to learn more about the behaviour of the building, especially in dynamic conditions. This framework is called Structural Health Monitoring and is mainly aimed at supervising historical buildings or crucial ones. Nowadays, Machine Learning has become one of the most popular topics and it is adopted in a large number of different case studies.