BACKGROUND/OBJECTIVES: Gastric cancer (GC) remains a significant global health burden due to its high mortality rate and frequent diagnosis at advanced stages. This study aimed to identify reliable diagnostic biomarkers and elucidate molecular mechanisms underlying GC by integrating transcriptomic data from independent platforms and applying machine learning techniques. METHODS: Two transcriptomic datasets from the Gene Expression Omnibus were analyzed: GSE26899 (microarray, = 108) as the discovery dataset and GSE248612 (RNA-seq, = 12) for validation. Differential expression analysis was conducted using limma and DESeq2, selecting genes with |log2FC| > 1 and adjusted < 0.05. The top 200 differentially expressed genes (DEGs) were used to develop machine learning models (random forest, logistic regression, neural networks). Functional enrichment analyses (GO, KEGG, Hallmark) were applied to explore relevant biological pathways. RESULTS: In GSE26899, 627 DEGs were identified (201 upregulated, 426 downregulated), with key genes including , , , , and . The random forest model demonstrated excellent classification performance (AUC = 0.952). GSE248612 validation yielded 738 DEGs. Cross-platform comparison confirmed 55.6% concordance among core genes, highlighting , , , , , , and . Enrichment analyses revealed involvement in ECM-receptor interaction, signaling, EMT, and cell cycle. CONCLUSIONS: This integrative transcriptomic and machine learning framework effectively identified high-confidence biomarkers for GC. Notably, , , , and emerged as consistent, biologically relevant candidates with strong diagnostic performance and potential clinical utility. These findings may aid early detection strategies and guide future therapeutic developments in gastric cancer.