Research on Delay-aware Fog Computing focuses on designing systems and strategies that minimize latency in processing, communication, and service delivery across fog and edge networks, which is critical for real-time and latency-sensitive applications. This area addresses challenges posed by dynamic workloads, heterogeneous resources, network congestion, and mobility of IoT devices. Key research directions include delay-aware task scheduling and resource allocation, predictive workload modeling for latency reduction, and latency-optimized service placement and migration strategies. Other emerging topics involve multi-objective optimization balancing delay, energy consumption, and resource utilization, edge–fog–cloud collaborative frameworks for low-latency processing, and network-aware routing protocols. Additionally, research on QoS- and SLA-compliant delay management, adaptive and fault-tolerant frameworks, and machine learning–driven delay prediction and mitigation represents significant avenues for advancing efficient, responsive, and latency-sensitive fog computing systems.