<p>Integrating Software-Defined Networking (SDN) into IoT (SD-IoT) emerges as a promising solution to the increasing complexity and scalability challenges of IoT networks. However, this convergence introduces significant challenges in network management. One of the critical challenges in SD-IoT is the controller placement problem (CPP), which has a substantial impact on network efficiency. Unlike traditional static controller placement strategies, CPP in SD-IoT becomes more challenging due to the heterogeneous and dynamic nature of IoT network infrastructure, where switches experience varying loads influenced by IoT device traffic patterns. In this paper, we propose a novel approach that combines the Grey Wolf Optimizer (GWO) with Weighted Betweenness Centrality (WBC)-based approach to address the controller placement problem. The approach leverages GWO to effectively explore the solution space, while WBC is used to strategically position controllers on nodes with high centrality. This ensures more effective controller deployment by minimizing network latency while optimizing switch assignment to controllers according to their loads. We evaluate the proposed method against existing approaches, including Optimal Placement, Betweenness Centrality with Hierarchical Clustering (HC-BC), and the Louvain Algorithm with Betweenness Centrality (Louvain-BC). The comparison is based on key performance metrics, such as switch to controller latency, inter-controller latency, flow rate, and execution time. Experimental results demonstrate that GWO-WBC-CPP achieves up to 19.89% reduction in switch-to-controller latency, 33.63% decrease in inter-controller latency, and 33.16% improvement in flow rate compared to HC-BC and Louvain-BC. Moreover, the GWO-WBC-CPP (50%) variant offers comparable performance while reducing inter-controller latency by up to 55.01%, making it highly suitable for large-scale or time-sensitive SD-IoT environments.</p>