The safe and efficient operation of hydrogen refueling stations is essential to support the global transition towards low-carbon energy systems. However, the scarcity of real-world operational data remains a major obstacle for advanced monitoring and anomaly detection. This study proposes a deep learning framework that combines LSTM-based synthetic data generation with unsupervised anomaly detection. A generative LSTM was used to simulate 756 realistic hydrogen refueling scenarios enriched with physically plausible anomalies. An LSTM-Autoencoder was subsequently trained to detect deviations in key process variables, achieving 92% accuracy for sustained anomalies, a 31% improvement compared to punctual anomaly detection. The proposed hybrid approach offers a scalable solution for intelligent diagnostics in emerging hydrogen infrastructure.