The increasing demand for real-time data processing has driven cloud, edge, and fog computing advances. While cloud computing excels at scalable and centralized resource management, it has limitations in latency-sensitive scenarios. The emergence of edge and fog environments enables distributed processing closer to data sources, improving latency and bandwidth efficiency for real-time applications. In these environments Scheduling remains a key challenge, involving workflow, resource, and task optimization under constraints such as execution time, cost, and energy consumption. Despite progress, green scheduling is underexplored, especially in hybrid environments. This paper reviews scheduling research across these environments, identifies gaps in green scheduling, and highlights the need for studies that integrate renewable energy and multi-objective optimization.