Software-Defined Networking (SDN) offers flexibility and
programmability in network management, but efficient resource
allocation remains a challenge due to dynamic traffic patterns and
diverse service requirements. This paper proposes an Enhanced
Ensemble Method (EEM) for optimizing resource allocation in SDN
environments. EEM integrates multiple ensemble learning techniques,
leveraging their complementary strengths to enhance prediction
accuracy and robustness. The key contribution lies in the novel
integration of ensemble methods tailored for SDN resource allocation,
offering improved adaptability to changing network conditions and
service demands. Evaluation on real-world SDN datasets demonstrates
that EEM outperforms existing methods in terms of both resource
utilization efficiency and service quality. Notably, EEM achieves
significant improvements in network throughput, latency reduction,
and resource utilization balance.