In various engineering fields, ensuring the health of utilized structures is crucial. Structural Health Monitoring (SHM) is the specific discipline aimed at assessing and maintaining the integrity of structures, enabling early fault detection, and extending their lifespan. In this work, we will present an application of Machine Learning (ML) as an auxiliary tool in the essential fault identification process within SHM. Specifically, we will analyze a case study with an emphasis on wind turbine blades, utilizing the data from this case as a basis to demonstrate the applicability of theoretical concepts. Among the techniques employed, we highlight the methodology focused on dimensional reduction, utilizing both traditional methods such as Principal Component Analysis and more modern approaches like feature engineering, coupled with highly adaptive ML models. These tools are crucial to harness the maximum potential of the vast amount of data generated by modern sensor and monitoring technologies. The integration of ML into SHM not only enhances precision in fault identification but also exemplifies the adaptability and effectiveness of innovative technologies in safeguarding structural health.