Green fog computing is an emerging research area that focuses on designing energy-efficient, sustainable, and environmentally friendly fog computing systems while maintaining high performance, low latency, and Quality of Service (QoS). Research papers in this domain explore techniques to reduce energy consumption and carbon footprint of fog nodes, including energy-aware task scheduling, workload consolidation, dynamic voltage and frequency scaling (DVFS), and resource virtualization strategies. Studies highlight optimization algorithms, heuristic and metaheuristic approaches, and machine learning techniques—including reinforcement learning and predictive analytics—for intelligent, context-aware, and adaptive energy management in fog environments. Recent works also investigate multi-tier fog–edge–cloud architectures to balance energy efficiency with scalability, fault tolerance, and service continuity. Security- and privacy-aware frameworks are increasingly integrated to ensure that energy-saving strategies do not compromise data confidentiality or system reliability. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and real-time multimedia services, where energy efficiency is critical for sustainable operations. Overall, research in green fog computing enables the design of adaptive, energy-efficient, and eco-friendly fog infrastructures that support sustainable, high-performance, and reliable next-generation computing ecosystems.