<![CDATA[Lung cancer continues to be a predominant global cause of death. Clinical outcomes are significantly influenced by the timely identification of high-risk populations, accurate diagnosis, and appropriate therapy selection. Progress in artificial intelligence (AI), liquid biopsy, and immunotherapy has opened new avenues for improving diagnostic accuracy and individualized treatment. Therefore, this review aimed to summarize the current evidence regarding the integration of artificial intelligence, liquid biopsy, and immunotherapy in improving lung cancer diagnosis, treatment selection, and personalized management. structured literature search was conducted in PubMed using keywords related to AI, liquid biopsy, immunotherapy, and lung cancer, with a focus on publications from the past 10 years. Of 18,169 records, 45 studies were selected after screening. AI-driven deep learning and radiomics enhance the precision of early detection, distinguish between benign and malignant nodules, and predict molecular changes, including EGFR, ALK, and PD-L1 expression. Liquid biopsy facilitates the non-invasive identification of biomarkers, such as ctDNA, CTCs, and exosomes, while AI enhances the interpretation of biomarkers and the characterization of cancer. AI models, such as CNN and multiomics methodologies, accurately predict treatment response and prognosis, thereby facilitating informed decision-making for immunotherapy. The combination of AI, liquid biopsy, and immunotherapy has significant potential to improve lung cancer management by enabling earlier diagnosis, personalized treatment, and improved response prediction]]>
