With this research, we apply range-resolved interferometry (RRI) to the maintenance of wind turbines using some of the most relevant machine-learning (ML) techniques. The degeneration of electrical and mechanical components of wind turbines can be predicted, detected, and anticipated using this method of automatic and autonomous learning. The vibrations in two different failure states are detected with the help of a scanner laser. In-process measurements taken by RRI agree with manual measurements, laser scanning measurements, and in-process hand measurements made following each working cycle. Consequently, the proposed method will be very useful for monitoring and diagnosing faults in wind turbines. The system will also be able to perform low-cost in-process measurements.