INTRODUCTION: Gastric cancer (GC) is one of the most frequently encountered malignant tumors in the clinic. Because effective early screening techniques are lacking, most patients have advanced disease at first diagnosis. The interleukin (IL)-36 family plays a vital role in regulating the immune system, inflammatory responses, and the occurrence and development of cancer. Hence, this study explored the potential role of IL-36 related genes (IL-36RGs) in GC and built a prognostic risk assessment model for GC based on IL-36RGs, which can help evaluate treatment and prognosis. METHODS: First, relevant datasets were downloaded from public databases. After processing the datasets to remove batch effects, perform differential analysis, and take intersections, IL-36-related differentially expressed genes (IL-36RDEGs) were screened. A prognostic risk model containing nine model genes was constructed based on univariate Cox and least absolute shrinkage and selection operator (LASSO) regression methods. Then, to investigate the potential biological activities of the model genes in GC, we conducted enrichment, PPI interaction network, and immune infiltration analyses. Immunohistochemical staining was conducted to validate the expression of IL-36A in GC. RESULTS: The prognostic risk model analysis revealed that mortality events in the high-risk group were substantially elevated compared to those in the low-risk group. The model demonstrated excellent predictive capability at 1, 2, and 3 years and showed the best clinical predictive performance at 3 years. Bioinformatics analysis of the model genes indicate that they primarily participate in mechanisms that promote the synthesis and secretion of cytokines in GC. And hub genes may be strongly correlated with host immune response mechanisms. According to the immunohistochemical staining results, IL-36A expression was higher in the STAD group than in the control group. CONCLUSIONS: The results of the above analysis suggest that IL-36RDEGs can serve as independent prognostic biomarkers for GC and provide insights into IL-36RGs from both bioinformatics and experimental validation perspectives.