Lung cancer, the malignancy with the highest global mortality rate, is currently undergoing a paradigm shift from precision medicine to intelligent medicine in its diagnostic and therapeutic models. Artificial intelligence (AI), leveraging its core advantages in multimodal data fusion and high-dimensional featureextraction, has deeply permeated the entire disease continuum of lung cancer screening, diagnosis, and individualized treatment. AI demonstrates significant value in improving patient outcomes and enhancing clinical efficiency, thereby reshaping the fundamental logic and clinical practice pathways of lung cancer management. This article reviews the latest advances of AI in real-world clinical diagnosis and treatment scenarios for lung cancer, with a focus on multimodal data fusion architectures, breakthrough applications of AI-assisted lung cancer diagnosis and treatment, and the practical barriers to clinical translation. It critically analyzes core challenges including data standardization, privacy protection, and ethical compliance. Finally, it envisions the future landscape of a nationwide intelligent ecosystem for lung cancer diagnosis and treatment—driven by federated learning and empowered by data elements—providing a reference for the standardized application and innovative development of AI in precision lung cancer care.