Congestion control remains a major challenge in improving the Quality of Service (QoS) for the Internet of Things (IoT), as the growing number of connected devices leads to network overload and data transmission delays. Although they may accommodate a large number of connections, modern wireless networks are restricted by their network resources. Congestion results, which has a detrimental effect on throughput, transmission latency, packet loss, power usage, and the longevity of the network as a whole. This challenge is addressed by introducing a novel congestion control method, Fuzzy C-Means used clustering for Round Trip Time (FCM-RTT). In this paper, the proposed FCM-RTT model clustering is used for fuzzy C-means. The final route layout for data transmission is then determined by rank computation. Three essential parts make up the routing configuration: first, a Round Trip Time (RTT) estimator that uses several techniques to gauge congestion levels A geodetic fuzzy subgraph rank (GFSR) computation that guarantees precise initial retransmission timeouts (RTO) comes in third, followed by an examination of trend and relative strength indicators. Using energy usage, routing overhead, and packet loss ratio, the suggested FCM-RTT strategy has been compared to other approaches. In comparison to RPR, CBR-RPL, ACW, and ECLRPL, the Experimental outcomes indicate that the FCM-RTT approach diminishes energy consumption by 43.58%, 25.8%, 14.82%, and 6.85%, respectively