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Wilms tumor (WT) is the most common pediatric renal malignancy. Reliable prognostic markers are crucial for improving patient outcomes. Immune-related genes (IRGs) significantly influence tumor progression and the tumor microenvironment, yet their prognostic value in WT remains unclear. This study aimed to develop an immune-related prognostic model for WT and investigate its underlying molecular and immunological mechanisms. We analyzed RNA-seq data and clinical information from the TARGET-WT and Gene Expression Omnibus databases. Using differential expression analysis, we identified differentially expressed genes. We identified immune-related differentially expressed genes (DEIRGs) by intersecting differentially expressed genes with known IRGs. Using univariate and multivariate Cox regression along with Least Absolute Shrinkage and Selection Operator regression, we selected 4 DEIRGs and constructed a prognostic risk score model. We further analyzed the model's molecular and immunological characteristics. Four DEIRGs (epidermal growth factor [EGF], teratocarcinoma-derived growth factor 1 [TDGF1], leukotriene B4 receptor [LTB4R], and HLA-DMB) showed significant associations with overall survival in WT patients. The risk stratification model categorized patients into high- and low-risk groups, with significantly poorer survival in the high-risk group (P < .001). Enrichment analysis revealed that the high-risk group showed enrichment in oncogenic pathways (e.g., genome instability), whereas the low-risk group demonstrated enrichment in immune defense and homeostasis pathways. The high-risk group exhibited reduced tumor microenvironment (TME) immunoreactivity, with EGF and LTB4R emerging as key regulatory factors. Both EGF and LTB4R demonstrate differential expression across multiple tumor types and correlate significantly with TME scores. The immune-related prognostic model developed in this study elucidates the regulatory roles of EGF and LTB4R in Wilms tumor progression. This model effectively stratifies patients, enables accurate prognosis prediction, facilitates individualized treatment planning, and identifies potential therapeutic targets.