Data clustering is an unsupervised technique that segregates data into multiple groups based on the features of the dataset. Soft clustering techniques allow an object to belong to various clusters with different membership values. However, there are some impediments in deciding whether or not an object belongs to a cluster. To solve these issues, an intuitionistic fuzzy set introduces a new parameter called hesitancy factor that contributes to the lack of domain knowledge. Unfortunately, selecting the initial centroids in a random manner by any clustering algorithm delays the convergence and restrains from getting a global solution to the problem. To come across these barriers, this work presents a novel clustering algorithm that utilizes crow search optimization to select the optimal initial seeds for the Intuitionistic fuzzy clustering algorithm. Experimental analysis is carried out on several benchmark datasets and artificial datasets. The results demonstrate that the proposed method provides optimal results in terms of objective function and error rate.