The threat of malicious content on a network requires network administrators and users to accurately detect desirable traffic flow into their respective networks. To this effect, several studies have found it imperative to classify traffic flow, and to use traffic classification in various applications such as intrusion detection, monitoring systems, as well as pattern detection in various networks. Research into machine learning techniques of clustering emerged due to the inefficiencies and drawbacks of the traditional port-based and payload-based schemes. The classic Kmeans technique of clustering, in combination with other methods and parameters, can be used to build newer unsupervised and semi-supervised approaches to meliorate the quality of service in networks. In this paper, we review twelve of the existing clustering techniques. The review covers their contribution to clustering methods, the existing challenges, as well as recommendations for further research in clustering traffic flows.