Image segmentation is a procedure in which an image is classified into different regions such that each region has the same features and distinctive characteristics. In the same segment, the pixels have similar characteristics whereas pixels which belong to different segments have significant variances. The division of the image into meaningful regions helps to make analysis easier and more effective. This is used in many applications including health care, pattern recognition, satellite imaging, etc. Clustering is a widely implemented unsupervised technique used for image segmentation mainly because of its easiness and fast computation and the Fuzzy c-means (FCM) clustering algorithm is among the most widely utilized for segmentation, however, there are certain weaknesses of the algorithm such as sensitivity to arbitrary initial values, and falling easily into a local optimum solution. To solve these limitations of standard FCM, various hybrid segmentation techniques have been proposed. In this paper, a new swarm based modified FCM algorithm for image segmentation is proposed. The proposed approach blends Dynamic Particle Swarm Optimization (DPSO) with FCM which enhanced the performance of FCM algorithm mainly because of the global optimization capability and parallel computing capability of DPSO. The values of inertia weight changes dynamically together with the fitness function. The proposed methodology to image segmentation is tested using various images and is compared with other existing conventional segmentation approaches by considering different evaluation parameters to determine the feasibility of the proposed schemes and experimental finding validates the superiority of the proposed method.