In modern networked systems, managing mean queuing delay is essential, particularly in environments where traffic is multi-class, bursty, and highly correlated. This study explores strategies for optimizing mean delay in queuing systems that handle diverse traffic classes with varying priorities and burstiness. Traditional delay control mechanisms often fail under conditions of irregular traffic, resulting in inefficiencies and increased latency. We propose a novel framework that integrates adaptive queuing policies with traffic prediction techniques to effectively reduce mean delay while maintaining service quality for all traffic classes. Simulation results demonstrate that our approach significantly reduces mean queuing delay across multi-class traffic scenarios compared to standard queuing models. This work has potential applications in high-demand environments such as telecommunications networks, cloud services, and data centers where efficient traffic handling is critical.