This paper introduces an innovative approach to the diagnosis of synchronous generator faults, which are critical to the stability of the power system. The method makes use of a hybrid Artificial Neural Network (ANN) model, which combines conventional ANN techniques with advanced machine learning algorithms. Even with noisy data, this hybrid model can detect a variety of faults with accuracy. Enhanced fault diagnosis in synchronous generators could result in better maintenance and increased system reliability. Enhancing fault diagnosis in synchronous generators through hybrid ANN technique offers a promising solution that will improve overall system reliability and maintenance. By combining ANN with the Ant–Lion Optimizer (ALO) algorithm, this work provides an advanced approach for diagnosing problems in synchronous generators, which are essential parts of power systems.