Resource management and provisioning in fog computing is a crucial research area that addresses the efficient allocation, scheduling, and orchestration of computational, storage, and network resources across geographically distributed fog nodes. Research papers in this domain explore strategies to ensure low-latency, energy-efficient, and QoS-compliant services for latency-sensitive and resource-intensive applications such as autonomous vehicles, industrial IoT, smart healthcare, and smart cities. Studies investigate static, dynamic, and adaptive resource management techniques, leveraging heuristic algorithms, optimization models, game-theoretic approaches, and machine learning—including reinforcement learning and predictive analytics—for intelligent and context-aware resource provisioning. Recent works also examine multi-tier fog–edge–cloud architectures to enhance scalability, resilience, and fault tolerance, while minimizing resource contention and bottlenecks. Security- and privacy-aware frameworks are increasingly integrated to protect sensitive data while maintaining efficient resource utilization. Overall, research in resource management and provisioning in fog computing focuses on creating adaptive, efficient, and reliable systems that optimize distributed resource utilization, ensure service continuity, and support next-generation IoT and edge applications.