The increasing demand for wind energy requires reliable predictive maintenance strategies to minimise premature failures caused by mechanical phenomena such as wear. Therefore, the early detection of failures is essential to improve the efficiency of predictive maintenance and ensure continuous electrical energy production. Over the last decade, data collected by the Supervisory Control and Data Acquisition (SCADA) system has become a common solution, especially due to its easily accessible data and cost-effective nature. This thesis focuses on developing various machine learning methods to establish an anomaly detection strategy, starting with the computation of a regression model, and concluding in an anomaly index that enables the assessment of the turbine’s health status.