Lung cancer remains the leading cause of cancer-related mortality worldwide, with prognosis strongly dependent on early detection and accurate diagnosis. Artificial Intelligence (AI) has emerged as a pivotal tool in lung cancer management, offering significant advancements in diagnostic precision, prognostic assessment, and treatment personalization. Our study provides a comprehensive overview of the evolution and current applications of AI in lung cancer, with a particular focus on radiological and histopathological diagnostics. Deep learning models, notably Convolutional Neural Networks (CNNs), have demonstrated remarkable efficacy in improving the detection, classification, and risk stratification of pulmonary nodules. Furthermore, AI-driven radiomics and computer-aided detection (CAD) systems have enhanced tumor characterization, facilitating more informed clinical decision-making. Despite these advancements, challenges persist, including issues related to data availability, validation methodologies, and regulatory compliance. Addressing these challenges through rigorous research and multicenter clinical trials will be essential for the successful integration of AI into routine clinical practice. AI has the potential to revolutionize lung cancer care by enabling earlier diagnosis, refining therapeutic strategies, and ultimately improving patient outcomes.