Image contrast is very important visual characteristics that will considerably improve the appearance of the image. In this paper image contrast is to be enhanced optimally to accurately portray all the data in the image using nature inspired meta-heuristic algorithms. Algorithms have been devised and proposed to enhance the contrast of low contrast images in this work. Poor image contrast caused by a low-quality capturing device, biased user experience, and an unsuitable environment setting during image capture is the main problem encountered during the image enhancement process. Histogram Equalization (HE), a frequently used technique for contrast enhancement, typically produces images with unwanted artifacts, an unnatural appearance, and washed-out appearances. The degree of enhancement is beyond the control of the global HE. The quality of an image is crucial for human comprehension, making image contrast enhancement (ICE) a crucial pre-processing stage in image processing and analysis. In the current study, the Pelican Optimization Algorithm, a contemporary meta-heuristic (MH) algorithm influenced by nature, is used as the foundation for the grayscale image contrast enhancement (GICE) approach (POA). The comparison of proposed method with existing contrast enhancement techniques has been done on the basis of standard image quality metrics. The proposed algorithm performance on standard test image and Kodak dataset demonstrates that total image contrast and information provided in the image are both greatly improved by the suggested POA-based image enhancement technique.