Immunotherapy efficacy in non-small cell lung cancer (NSCLC) remains variable

with traditional biomarkers (programmed death-ligand 1 [PD-L1] and tumor mutational burden) limited by heterogeneity and sensitivity/specificity constraints. This review evaluates the role of artificial intelligence (AI) in advancing predictive biomarkers through the integration of radiomics

pathomics

and multi-omics. We conducted a narrative literature review (PubMed; Web of Science; chictr.org.cn; clinicaltrials.gov

2012–2025) using keywords spanning NSCLC

immunotherapy

and AI methodologies (radiomics

pathomics

and multi-omics). The inclusion criterion was studies that developed predictive models for immune checkpoint inhibitor outcomes; the exclusion criteria were non-original research and preclinical studies. Emerging evidence highlights AI’s transformative role in the prediction of immunotherapy efficacy for treating NSCLC: radiomics quantifies tumor heterogeneity through computed tomography-based PD-L1 and TMB ≥10 mut/Mb biomarkers

while pathomics refines PD-L1 scoring and immune microenvironment classification. Multi-omics integration synergizes imaging

histopathology

and liquid biopsy data with dynamic models by resolving spatial discordance and temporal clonal evolution. However

clinical adoption requires the standardization of peri-tumoral radiomics

validation of delta-feature protocols

and prospective trials to address overfitting risks in small cohorts. AI-driven multi-omics redefines immunotherapy prognostication

but faces interpretability and ethical challenges. Future validation must prioritize prospective multicenter trials

causal biological linking of radiomic features

and federated learning frameworks to ensure equitable clinical implementation.