Lung cancer remains the leading cause of cancer incidence and mortality worldwide, and surgically resectable disease represents a critical window for cure. However, the current care continuum for resectable lung cancer remains limited by heterogeneous risk profiles, empirical decisions, fragmented integration of multi-dimensional information, and insufficient prediction of clinical outcomes. Under these circumstances, artificial intelligence (AI) has emerged as a promising approach for extracting clinically relevant information from complex data. Nevertheless, a synthesis of AI evidence across the full pathway of resectable lung cancer remains lacking, leaving clinicians uncertain about its clinical implementation. To address this gap, we narratively synthesize representative evidence regarding the evolution, technical platforms, and clinical applications of AI in resectable lung cancer, integrating how AI could support preoperative screening and diagnosis, intraoperative decision-making and procedures, as well as postoperative treatment and surveillance. We further discuss the major barriers to clinical translation, including limited external and prospective validation, patient safety, and accountability, followed by future directions. At present, many AI applications remain at an early exploratory stage, and further prospective evaluation is required to establish their clinical utility.