Medical image enhancement is considered as a challenging image-processing framework because the low quality of images resulted after acquisition and transmission seriously affects the clinical diagnosis and observation. In order to improve the image visual quality, a novel medical image enhancement algorithm that is based on the contrast limited adaptive histogram equalization and pelican optimization algorithm is proposed in this work. The primary step of the process is the medical generation using Text-to-image generative model. Then, the estimation of the clip-limit, which controls the enhancing performance. Finally, the operation of enhancing the medical images using our proposed method. As a conclusion, the simulation experiments prove that our proposed algorithm achieves superior performance qualitatively and quantitatively, compared with the state-of-the-art experimental methods. Furthermore, the advantageous characteristic of this algorithm is its applicability in multiple types of images. In this basis, the improvement of the medical images’ quality using our algorithm allows attaining a superior visual impact on the processed image and increase the rate of conformity in the clinical diagnosis.