Selecting a deserving node as a cluster head (CH) has a symbolic impact on the energy efficient working of Wireless Sensor Networks (WSNs). With the reduction in network’s energy utilization, its lifetime can be increased up to a great extent. Clustering is a mechanism that helps in addressing both these issues efficiently. However, clustering also faces few challenges like making adequate decision during cluster head selection. This challenge arises due to the uncertain and dynamic nature of WSNs originated because of the random deployment of the sensor nodes in them. This paper tries to illustrate the potential of one of the soft computing paradigms i.e. fuzzy logic in overwhelming these issues in clustering WSNs. Since, Fuzzy Logic (FL) has the capability of simulating the mental process of human mind to observe, learn and understand things in the presence of uncertainty. So, this paper presents a systematic review of different fuzzy and hybrid fuzzy based approaches for handling clustering in WSNs.