Lung cancer is one of the leading causes of cancer-related mortality worldwide, and early detection is crucial for improving survival rates. Traditional diagnostic methods, such as computed tomography (CT) scans and biopsies, are effective but often costly, invasive, and inaccessible in resource-limited settings. This study proposes a novel, non-invasive approach to lung cancer pre-scanning based on iris pattern analysis using machine learning techniques. The study explores the hypothesis that systemic diseases, including lung cancer, manifest detectable changes in the iris. A dataset of iris images from healthy individuals and lung cancer patients was processed using feature extraction methods, followed by classification using machine learning algorithms. The proposed approach demonstrates promising accuracy in distinguishing lung cancer patients from healthy individuals, highlighting the potential of iris-based screening as an early, cost-effective, and non-invasive tool for lung cancer detection. Further study and clinical validation are necessary to integrate this technique into real-world diagnostic workflows.