This study analyzes the effectiveness of the Oklab color space in color image segmentation compared with alternative color spaces. The potential benefits of Oklab in the conventional Fuzzy c-Means (FCM) segmentation method were investigated. A comprehensive comparison with commonly used color spaces such as RGB, HSV, and CIELAB was conducted to evaluate their performance across the images in the BSD500 dataset. Experiments included variations in the cluster count parameter for FCM. The accuracy of image segmentation was measured using the F-score, Adjusted Rand Index (ARI), and variation of the information (VI). Furthermore, the consistency of Oklab’s performance across various FCM cluster configurations reinforces its suitability for challenging image segmentation scenarios, providing a promising alternative for practical implementations. The experimental results demonstrate that the Oklab color space consistently outperforms the other color spaces in terms of segmentation accuracy and error trade-offs, suggesting potential for more accurate results in FCM-based image segmentation.