Cloud–fog collaboration is a significant research area that focuses on integrating cloud and fog computing infrastructures to leverage their complementary strengths—cloud’s high computational power and storage capacity, and fog’s low-latency, proximity-based processing—for enhanced performance, scalability, and Quality of Service (QoS). Research papers in this domain explore architectures, frameworks, and algorithms that enable seamless task offloading, workload distribution, service orchestration, and resource management across distributed cloud–fog environments. Studies highlight static, dynamic, and adaptive collaboration strategies, including optimization models, heuristic and metaheuristic algorithms, and machine learning techniques for intelligent and context-aware task scheduling, resource allocation, and load balancing. Recent works also investigate multi-tier cloud–fog–edge architectures to enhance fault tolerance, scalability, and service continuity while minimizing latency and energy consumption. Security- and privacy-aware collaboration frameworks are increasingly emphasized to protect sensitive data during inter-layer communications and task execution. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and latency-sensitive multimedia services. Overall, research in cloud–fog collaboration enables adaptive, efficient, and resilient integration of distributed computing resources, ensuring high-performance, low-latency, and reliable services in next-generation computing ecosystems.