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Background/Objectives: Mammary gland traits (milk quality and lactation performance) are economically critical for B. taurus and O. aries, but core regulatory hub genes remain unclear due to high false positives in single-method transcriptomic analyses. This study aimed to identify robust hub genes linked to species-specific differences in mammary gland tissue via an integrated bioinformatics strategy. Methods: Raw transcriptomic data (77 B. taurus and 77 O. aries mammary gland samples) were retrieved from the European Nucleotide Archive (ENA); after quality control, differential expression gene (DEG) screening, weighted gene co-expression network analysis (WGCNA), and SHapley Additive exPlanations (SHAP)-assisted machine learning were performed, with core genes defined as the intersection of the three gene sets, and functional enrichment and protein–protein interaction (PPI) network analyses were used to prioritize hub genes. Results: A total of 13,138 high-quality genes were retained, including 6148 DEGs, 4698 WGCNA core module genes, and 500 SHAP-high-contribution genes, yielding 178 core genes that were significantly enriched in the “translation” (p < 0.001) pathways; hub genes were identified via PPI network analysis. Conclusions: These findings indicate that RPS15 and RPL7A are core species-difference signals in mammary gland tissue of B. taurus and O. aries, providing insights into inter-species molecular differences, and this integrated strategy enhances the robustness of hub gene identification in pure bioinformatics studies.