There is a growing need for plenarily automated algorithms that expeditiously localize the optic disc region in retinal fundus images for the analysis of retinal pathologies such as glaucoma. In this paper, we propose a methodology based on particle swarm optimization for automatic localization of optic disc region from retinal fundus images, where minimization of the fitness function is utilized to resolve optimization quandaries. Here, kernels are modeled as particles and they test the region-of-interest based on the fitness function, in the respective databases, where it is likely that the optic disc exists. The proposed method is validated on a total of 1670 fundus images obtained from various publicly available fundus image datasets. The optic disc localization accuracy obtained by the proposed method are 100%, 98.01%, 96.15%, 98.87%, 100%, and 100% on DRIVE, DRISHTI-GS, DIARETDB0, DIARETDB1, DRIONS-DB, and MESSIDOR fundus image databases, respectively. The precision of localization was improved with initialization of kernel particles within bright region-of-interest in fundus images.