Fog computing has emerged as a disruptive paradigm that extends cloud services to the network’s edge, permitting more effective resource management and improved performance across a wide range of applications, frequently those requiring low latency and high bandwidth. This research paper offers a thorough analysis of fog-enabled cloud computing systems’ resource management strategies. We thoroughly review many methodologies, including resource allocation, scheduling, load balancing, and data management, to evaluate their competence in optimizing resource utilization while minimizing latency and energy consumption. We highlight the challenges difficult in implementing these explanations, such as dynamic resource allocation, security concerns, and device heterogeneity. Additionally, we review present research trends, technical advancements, and potential future initiatives in resource management for fog computing. By synthesizing existing research, this study objectives to provide insights into the design and implementation of robust resource management methods that can progress the scalability and performance of cloud computing systems empowered by fog. The analysis of fog-enabled cloud computing resource management solutions highlights the importance of efficient resource allocation and management in developing the effectiveness, scalability, and responsiveness of fog computing systems. By examining numerous strategies and their implications, this study emphasizes the need for adaptive and intelligent resource management frameworks that can dynamically respond to varying workloads and user demands. The integration of fog computing with cloud resources offers significant advantages, including reduced latency, improved data processing capabilities, and enhanced support for IoT applications.