Background: Stroma in the tumor microenvironment (TME) is known to impact prognosis and responses to therapy. Few mathematical models exist to prognosticate patients, based on mRNA expressivity in the TME. Methods: Clinical outcomes data and mRNA-seq of 98 patients with stage III estrogen receptor (ER) positive (+) and HER2 negative (-) breast cancer were obtained from TCGA. Twenty six gene groups composed of 191 genes (refer to presentation) enriched in cellular and non-cellular elements of TME, mutational burden (MB), and clinical data were analyzed by Kaplan-Meier (KM) analysis and multivariate nonlinear regression assisted by machine learning to achieve confined optimization with model-data minimization among multiple distribution functions. Results: Prognostication was modeled with higher risk score (RS) representing worse prognosis in stage 3 ER+HER2- breast cancer. Fifteen genes (CD8A, CD8B, FCRL3, GZMK, CD3E, CCL5, TP53, ICAM3, CD247, IFNG, IFNGR1, ICAM4, SHH, HLA-DOB, CXCR3) and five genes (LOXL2, PHEX, ACTA2, MEGF9, TNFSF4) out of 191 genes associated with good and poor prognosis were identified. Genomic expression of the fifteen and five gene groups were labeled as G and P, respectively. RS = 9.3185 – 0.3250 × (Age at diagnosis^0.0001) – 8.2979 × (P/G^−0.0051). Based on RS, patients were clustered into two groups; high and low RS groups, showing two KM curves with P = 0.05, HR = 2.878 (95% CI 1.903 – 3.471), confirming the validity of RS modeling. Analysis of immune profiles in high and low RS groups shows that expression of genes associated with desmoplastic reaction, neutrophils, and immunosuppressive cytokines are higher in high RS groups; and those related to immune system activation are higher in low RS groups (p < 0.05). Conclusions: Machine learning-assisted mathematical modeling of RS and gene analysis identified TME-related genes and gene groups that are strongly associated with worse prognosis in stage 3 ER+HER2- breast cancer. RS could potentially prognosticate patients in the clinic with available genomic profiles.