Lung cancer screening (LCS) has evolved significantly through technological innovation, pivotal clinical trials, and refined guidelines. This chapter traces the shift from early screening methods like chest X-rays and sputum cytology to the transformative adoption of low-dose computed tomography (LDCT). Seminal studies such as the Mayo Lung Project, Early Lung Cancer Action Project (ELCAP), and the National Lung Screening Trial (NLST) established LDCT as the gold standard for detecting lung cancer in high-risk populations, demonstrating significant reductions in mortality. The NELSON trial further validated LDCT’s utility, emphasizing volumetric nodule assessment and improved outcomes among women and high-risk groups. Despite these advances, disparities in screening access and eligibility persist, particularly due to earlier USPSTF and CMS guidelines that applied rigid age and smoking history criteria, disproportionately excluding many Black individuals, women, and people of lower socioeconomic status. In response, risk prediction models like the PLCOm2012 have emerged to address these inequities by incorporating a wider range of demographic and clinical variables. The integration of structured reporting and tobacco cessation counseling into LDCT programs further enhances their impact by ensuring continuity of care and addressing root causes of lung cancer. Looking ahead, artificial intelligence (AI) models such as Sybil offer transformative potential to refine risk stratification, enhance workflow efficiency, and further reduce disparities in early detection. Together, these innovations represent a comprehensive, equity-focused evolution in lung cancer screening that prioritizes personalized care, population health, and earlier diagnosis to improve outcomes for those at greatest risk.