<p>Breast cancer remains one of the most invasive diseases affecting women worldwide, despite advancements in detection and treatment. A major diagnostic challenge arises from the poor quality of mammography images, especially those with low contrast, as they frequently impede accurate visual interpretation of cancerous regions. To address this issue, this study proposes a novel approach that combines the Cuckoo Search Algorithm (CSA) with Histogram Equalization (HE) to enhance the visual quality of medical images. The novelty of this work lies in the integration of CSA, a nature-inspired metaheuristic optimization algorithm, with HE to overcome the limitations of traditional contrast enhancement techniques, such as over-enhancement and loss of brightness. The proposed method leverages the global optimization capabilities of CSA to fine-tune the parameters of HE, ensuring optimal contrast enhancement while preserving image brightness and minimizing artifacts. Extensive experiments were conducted on diverse medical images from the Digital Mammography Screening Database (DDSM) to evaluate the method’s performance. Comparative analysis with state-of-the-art techniques demonstrates the superiority of the proposed approach across multiple metrics, including entropy, structural similarity index (SSIM), contrast improvement index (CII), peak signal-to-noise ratio (PSNR), mean square error (MSE), and computational efficiency. The results highlight the method’s ability to enhance image contrast effectively while maintaining high visual quality, making it a valuable tool for radiologists in disease inspection and analysis. This research contributes to the on-going advancement of medical imaging technologies by offering a robust and efficient solution for improving the accuracy of breast cancer diagnosis.</p>