This research presents a comprehensive study on the efficacy of combining particle swarm optimization (PSO) with histogram equalization (HE) for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. The study highlights that HE preprocessing significantly stabilizes and enhances the convergence of the PSO algorithm, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision, highlighting the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation.This research presents a comprehensive study on the efficacy of combining particle swarm optimization (PSO) with histogram equalization (HE) for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. The study highlights that HE preprocessing significantly stabilizes and enhances the convergence of the PSO algorithm, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision, highlighting the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation.