The field of energy management systems is undergoing a significant transformation with the integration of artificial intelligence and machine learning to enhance cyber resilience. Researchers are exploring innovative solutions, including federated learning, anomaly detection, and mitigation techniques, to maintain operational reliability and economic efficiency in microgrid energy management systems under cyberattack conditions. Notable advancements include the development of comprehensive cyber-resilient frameworks, novel two-stage cascade false data injection attack detection, and energy management system optimization. The incorporation of AI-enabled hybrid cyber-physical frameworks for adaptive control in smart grids is also a significant development, leveraging machine learning-based digital forensic frameworks to detect, identify, and mitigate security incidents in real-time. Other areas, such as urban modeling and energy systems, foundation models, access control, and language models, are also witnessing significant shifts towards more scalable, privacy-preserving, and integrated approaches. The use of large language models is showing promising results in improving accuracy, efficiency, and interpretability in various domains, including automated homework assessment, IoT intrusion detection, and power system transient stability assessment. However, challenges such as scalability, reliability, and transparency remain to be addressed. Overall, the field is moving towards more innovative and effective AI-driven solutions for energy and cybersecurity challenges, with a focus on safety, security, and responsible AI development and deployment.