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Lung cancer remains one of the leading causes of mortality worldwide. Early detection is crucial for improving patient survival rates. Artificial Intelligence (AI) has emerged as a powerful tool in the medical field, enhancing diagnostic accuracy and efficiency. This paper explores AI-based techniques for lung cancer detection, including machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs). We discuss their applications in medical imaging, biopsy analysis, and predictive analytics. Furthermore, the study highlights challenges, including data scarcity, model interpretability, and clinical integration. AI-based lung cancer detection promises improved diagnosis, cost-effectiveness, and enhanced patient outcomes.