<p>Dioxins are persistent environmental pollutants and key components of the human exposome with established carcinogenic potential. As airborne toxicants, they link environmental pollution to lung adenocarcinoma (LUAD), yet their molecular mechanisms remain unclear. Dioxin-interacting genes were curated from toxicogenomic databases and intersected with LUAD differentially expressed genes to identify dioxin-related molecular signatures. Consensus clustering was performed to define LUAD subtypes with distinct clinical and transcriptomic characteristics. Weighted gene co-expression network analysis (WGCNA) was applied to identify key gene modules associated with dioxin exposure and tumor progression. Hub genes were further integrated into diagnostic and prognostic models using multiple machine-learning algorithms based on both tumor tissue and peripheral blood transcriptomic datasets. Genome–exposome interactions were explored through pathway enrichment and chemical–gene interaction analyses. Single-cell RNA sequencing data were used to characterize cell-type–specific expression patterns. In vitro functional assays were conducted to validate the biological roles of candidate genes. Both models demonstrated robust predictive performance across cohorts. SLC15A2 was consistently identified as a hub gene and showed predominant expression in lung epithelial cells based on single-cell RNA sequencing. Pan-cancer analyses revealed significant dysregulation of SLC15A2, with lower expression in LUAD associated with poorer survival. Functional experiments confirmed that SLC15A2 overexpression suppressed LUAD cell proliferation and invasion, supporting a tumor-suppressive role. This integrative exposome–genome analysis highlights dioxin-related transcriptomic dysregulation in LUAD and identifies SLC15A2 as a potential tumor suppressor and biomarker for precision stratification.</p>