Centrifugal pumps, as key components of hydraulic systems, play a fundamental role in ensuring the reliable operation of numerous industrial processes in sectors such as energy, chemical production, and oil refining, where uninterrupted equipment performance is of critical importance. Failures of centrifugal pumps can result in substantial financial losses due to costly repairs and unplanned production downtime. This paper presents an innovative approach to diagnosing and detecting faults in centrifugal pumps. The method is based on the application of Motor Current Signature Analysis (MCSA) in combination with automated machine learning (AutoML) technologies. Such an approach enables efficient and highly accurate identification of early signs of equipment malfunction. The experimental study was conducted using an open dataset collected under conditions close to real-world operation. The proposed method achieved a fault detection accuracy of 89 %, which significantly exceeds the performance of the traditional gradient boosting method. This confirms the advantage of a comprehensive model developed through AutoML. Further improvement in diagnostic accuracy was made possible by applying an enhanced Park’s vector transformation to the raw current and voltage data. This approach makes it possible to detect even subtle anomalies in pump operation, thereby strengthening the capability for early fault prediction. The study not only highlights the potential of MCSA as a non-invasive and scalable tool for equipment condition monitoring but also demonstrates the promise of AutoML for technical diagnostics of industrial pumps.