The integration of artificial intelligence (AI) into lung cancer care marks a transformative shift in how the disease is detected, diagnosed, and treated. This literature review examines developments from 2010 to 2023, focusing on 55 peer-reviewed studies sourced systematically through IEEE Xplore, Scopus, and PubMed using the PRISMA framework. The findings highlight the growing role of machine learning (ML) methods—particularly convolutional neural networks (CNNs) and natural language processing (NLP)—in enhancing diagnostic accuracy and clinical decision-making. CNNs have demonstrated strong capability in distinguishing malignant from benign pulmonary nodules, reducing false positives and limiting unnecessary interventions. Meanwhile, NLP techniques enable the extraction of critical insights from unstructured clinical notes and electronic health records, enriching patient profiling and improving individualized care. These AI-driven tools often outperform conventional statistical models in predictive accuracy and contribute significantly to personalized treatment strategies. In addition, the use of multi-omics data and AI-enabled Clinical Decision Support Systems (CDSS) are emerging as powerful means for refining therapeutic decisions. However, several challenges remain, including data heterogeneity, model transparency, and the seamless integration of AI systems into existing healthcare workflows. This review underscores the importance of interdisciplinary collaboration and ongoing refinement of AI models to ensure their ethical, effective, and equitable deployment in real-world oncology settings.