Background: Current radiomic non-small cell lung cancer prognostic models predominantly depend on statistical correlations, lacking robust biological validation. This study integrates multi-omics data to develop a preoperative computed tomography (CT) radiomics model, systematically elucidating its biological links to tumor molecular heterogeneity, immune microenvironment, and clinicopathological phenotypes, advancing clinical translation of radiomics. Methods: This retrospective study analyzed 334 surgically resected stage I-IIIA NSCLC patients. Radiomic features were extracted from preoperative contrast-enhanced CT images. LASSO-Cox regression developed the Rad-score. Cross-cohort validation applied fixed feature thresholds. Integrated gene set enrichment analysis, differential gene expression, and immune microenvironment analyses revealed biological disparities between radiomics risk-stratified groups. Integrated clinicopathological data explored radiomics risk stratification and clinical phenotype associations, constructing a tripartite cross-scale explanatory framework of radiomics-genomics-clinical phenotypes. Results: The Rad-score demonstrated robust prognostic stratification capacity across the training, internal validation, and external validation cohorts. Gene set enrichment analysis revealed significant enrichment of tumor invasion and proliferation-related pathways—including hypoxia, TNFA-NF-κB signaling, inflammatory response, and angiogenesis—in the high-risk group. Differential gene analysis further identified marked disparities in cell cycle regulation, DNA repair, and platinum resistance between risk groups. Immune microenvironment profiling showed significantly reduced immune scores and decreased proportions of naive B cells in high-risk patients, indicating impaired immune activity. At the macro level, the high-risk group exhibited stronger inflammatory responses, more aggressive clinicopathological phenotypes, and poorer nutritional status, mutually validated by micro-genomic characteristics. Conclusion: This study demonstrates that radiomics can non-invasively reveal tumor molecular heterogeneity and immune microenvironment characteristics, elucidating direct associations between imaging features and tumor biological behavior. These findings provide a critical theoretical foundation for the clinical translation of radiomics.