Weather radars play a crucial role in monitoring atmospheric conditions, especially in regions prone to adverse weather phenomena and near critical infrastructure hubs like airports. Ensuring their continuous and reliable operation is essential for accurate weather forecasting and early warning systems. This paper addresses the challenge of acoustic anomaly detection in weather radar systems through a novel approach using augmented spectrograms and autoencoders. We detail the development of a model capable of distinguishing between normal and abnormal operational sounds, an essential step toward effective predictive maintenance. Our method demonstrates outstanding performance, particularly on a dataset provided by the Hellenic Air Force, achieving an AUC of 1.00, signifying flawless anomaly detection. The study’s outcomes highlight the potential of machine learning in reducing the need for manual inspections and pave the way for further exploration in applying these techniques in real-world scenarios for enhanced radar system reliability.