Artificial intelligence (AI) is transforming the management of non-small cell lung cancer (NSCLC) by enabling more accurate and personalized treatment decisions. This review highlights the potential of AI technologies, particularly radiomics and pathomics, in improving diagnostic, prognostic, and therapeutic approaches for NSCLC. Radiomics involves the analysis of medical images to extract features that can predict treatment response and patient outcomes, while pathomics uses AI to analyze tissue samples for molecular and genetic information. These technologies help overcome limitations in traditional methods, such as the inability to assess tumor heterogeneity and the need for invasive biopsies. AI-based approaches can provide more accurate predictive biomarkers, such as PD-L1 expression and tumor mutational status, which are crucial for determining the effectiveness of immunotherapy and targeted treatments. Despite these advancements, challenges remain, including the need for standardized protocols, the interpretation of AI results, and the integration of AI into clinical practice. The future of AI in NSCLC management includes the use of radiomics and liquid biopsies to develop novel companion diagnostics, enabling more precise and individualized treatment strategies. Overall, AI has the potential to significantly enhance the management of NSCLC by improving diagnostic accuracy, treatment planning, and patient outcomes.Artificial intelligence (AI) is transforming the management of non-small cell lung cancer (NSCLC) by enabling more accurate and personalized treatment decisions. This review highlights the potential of AI technologies, particularly radiomics and pathomics, in improving diagnostic, prognostic, and therapeutic approaches for NSCLC. Radiomics involves the analysis of medical images to extract features that can predict treatment response and patient outcomes, while pathomics uses AI to analyze tissue samples for molecular and genetic information. These technologies help overcome limitations in traditional methods, such as the inability to assess tumor heterogeneity and the need for invasive biopsies. AI-based approaches can provide more accurate predictive biomarkers, such as PD-L1 expression and tumor mutational status, which are crucial for determining the effectiveness of immunotherapy and targeted treatments. Despite these advancements, challenges remain, including the need for standardized protocols, the interpretation of AI results, and the integration of AI into clinical practice. The future of AI in NSCLC management includes the use of radiomics and liquid biopsies to develop novel companion diagnostics, enabling more precise and individualized treatment strategies. Overall, AI has the potential to significantly enhance the management of NSCLC by improving diagnostic accuracy, treatment planning, and patient outcomes.