The problem of network congestion control remains a critical issue and a high priority, especially given the increased demand to use the Internet for time/delay-sensitive applications with differing Quality of Service (QoS) requirements (e.g. Voice over IP, video streaming, Peer-to-Peer, interactive games). Despite the many years of research efforts and the large number of different control schemes proposed, there are still no universally acceptable congestion control solutions. Thus, even with the classical control system techniques used from various researchers, these still do not perform sufficiently to control the dynamics, and the nonlinearities of the TCP/IP networks, and thus meet the diverse needs of today's Internet. Given the need to capture such important attributes of the controlled system, the design of robust, intelligent control methodologies is required. Consequently, a number of researchers are looking at alternative non-analytical control system design and modeling schemes that have the ability to cope with these difficulties in order to devise effective, robust congestion control techniques as an alternative (or supplement) to traditional control approaches. These schemes employ fuzzy logic control (a well-known Computational Intelligence technique). In this chapter, we firstly discuss the difficulty of the congestion control problem and review control approaches currently in use, before we motivate the utility of Computational Intelligence based control. Then, through a number of examples, we illustrate congestion control methods based on fuzzy logic control. Finally, some concluding remarks and suggestions for further work are given.