The article reviews the role of artificial intelligence (AI) in the management of non-small cell lung cancer (NSCLC). It highlights the potential of AI-driven technologies, particularly radiomics and pathomics, in optimizing patient-centered treatment decisions. Radiomics leverages imaging data to extract subtle tumor features, providing unbiased information for diagnosis, prognosis, and treatment response prediction. Pathomics, on the other hand, integrates multiomics data from tissue biopsies to improve diagnostic, prognostic, and predictive capabilities. The review discusses the current state of AI applications in NSCLC, including the use of machine learning (ML) and deep learning (DL) algorithms to predict treatment efficacy, assess biomarkers, and monitor individualized responses. Despite the promising outcomes, the article also addresses the challenges and limitations, such as feature reproducibility, technical requirements, and the need for ethical and legal frameworks. The authors conclude that while AI holds significant potential, further validation and implementation in large-scale studies are necessary to realize its full benefits in personalized NSCLC management.The article reviews the role of artificial intelligence (AI) in the management of non-small cell lung cancer (NSCLC). It highlights the potential of AI-driven technologies, particularly radiomics and pathomics, in optimizing patient-centered treatment decisions. Radiomics leverages imaging data to extract subtle tumor features, providing unbiased information for diagnosis, prognosis, and treatment response prediction. Pathomics, on the other hand, integrates multiomics data from tissue biopsies to improve diagnostic, prognostic, and predictive capabilities. The review discusses the current state of AI applications in NSCLC, including the use of machine learning (ML) and deep learning (DL) algorithms to predict treatment efficacy, assess biomarkers, and monitor individualized responses. Despite the promising outcomes, the article also addresses the challenges and limitations, such as feature reproducibility, technical requirements, and the need for ethical and legal frameworks. The authors conclude that while AI holds significant potential, further validation and implementation in large-scale studies are necessary to realize its full benefits in personalized NSCLC management.