This work introduces a novel method to detect and quantify uniform pitch misalignment in wind turbines. This fault, where all blades are equally misaligned, is hard to detect because it causes no immediate imbalance but reduces efficiency over time. By combining physics-based features with machine learning techniques, our approach reliably identifies and quantifies this fault under various wind regime conditions, improving turbine maintenance and energy production.