Image enhancement plays an important role in medical imaging. It improves the visual quality of images, remove noise and artifacts. This systematic literature review examines the variants of histogram equalization techniques in medical image enhancement. These techniques widely used for improving contrast and detail in imaging modalities such as brain MRI, mammogram, CT scans, and X-rays. The review explores various histogram equalization techniques, including adaptive, contrast-limited, and brightness-preserving techniques, focusing on their advancements, key features, advantages, and limitations. The datasets and evaluation metrics used to assess these techniques effectiveness and relevance are discussed. This review highlights how histogram equalization techniques enhance diagnostic quality, address their challenges, and evaluate their impact on diverse medical imaging data by providing a comprehensive analysis. This study is a valuable resource for researchers aiming to apply or advance histogram equalization techniques for improved medical image interpretation and diagnostic accuracy.