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Image thresholding is a process that separates particular object within an image from their background. An optimal thresholding technique can be taken as a single objective optimization task, where computation and obtaining a solution can become inefficient, especially at higher threshold levels. In this paper, a new and efficient color image multilevel thresholding approach is presented to perform image segmentation by exploiting the correlation among gray levels. The proposed method incorporates gray-level co-occurrence matrix (GLCM) and cuckoo search (CS) in order to effectively enhance the optimal multilevel thresholding of colored natural and satellite images exhibiting complex background and non-uniformities in illumination and features. The experimental results are presented in terms of mean square error (MSE), peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity …