In this study, the synthetic time-series data with temperature, pressure, vibration, oil condition, emission measurement, and operating parameters are used with focus on AI-based techniques for fault diagnosis and predictive maintenance in gas turbine operations. The dataset was split into three conditions of 70% normal, 20% minor faults, and 10% severe faults. Three machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks—were compared in terms of accuracy, precision, recall, and F1-score. Among these three, LSTM provided superior performance with 95.3% accuracy, precision of 0.94, recall of 0.95, and an F1-score of 91.2%, while RF and SVM were far behind. By managing temporal dependencies within the data effectively, the LSTM model enhanced unplanned downtime by 42% compared to conventional reactive maintenance strategies. These results illustrate the potential of AI-based methods, namely LSTM, to provide evidence-based predictive maintenance approaches and help asset managers with cost-saving and effective solutions for operating energy systems.