The rapid evolution of multi-scale energy systems (spanning electricity, hydrogen, and renewable integration) has introduced unprecedented complexity, making robust anomaly detection a critical challenge. The vast heterogeneity and dynamic nature of these systems expose them to faults and cyber–physical risks, where timely detection is vital to ensure resilience, safety, and uninterrupted operation. Nowadays, deep learning (DL) techniques have emerged as powerful tools for modeling large-scale, non-linear, and high-dimensional energy data, enabling the extraction of latent spatio-temporal patterns. In this paper, we proposed an optimized deep reinforcement learning–generative adversarial network (ODRL-GAN) framework for reliable anomaly detection in multi-scale energy systems. The integration of DRL and GAN brings a key innovation: while DRL enables adaptive decision-making under dynamic operating conditions, GAN enhances detection by reconstructing normal patterns and exposing subtle deviations. To further strengthen the model, a novel multi-objective chimp optimization algorithm (NMOChOA) is employed for hyper-parameter tuning, improving accuracy, and convergence. This design allows the ODRL–GAN to effectively capture high-dimensional spatio-temporal dependencies while maintaining robustness against diverse anomaly patterns. The framework is validated on two benchmark datasets, PSML and LEAD1.0, and compared against state-of-the-art baselines including transformer, deep belief network (DBN), convolutional neural network (CNN), gated recurrent unit (GRU), and support vector machines (SVM). Experimental results demonstrate that the proposed method achieves a maximum detection accuracy of 99.58% and recall of 99.75%, significantly surpassing all baselines. Furthermore, the model exhibits superior runtime efficiency, faster convergence, and lower variance across trials, highlighting both robustness and scalability. The optimized DRL–GAN framework provides a powerful and generalizable solution for anomaly detection in complex energy systems, offering a pathway toward secure and resilient next-generation energy infrastructures.