Immune-related　genes　(IRGs)　play　a　pivotal　role　in　the　tumor　microenvironment　of　triple-negative　breast　cancer　(TNBC).　This　study　aimed　to　uncover　novel　prognostic　markers　for　TNBC　based　on　IRGs.　Differential　expression　analysis,　univariate　Cox　regression,　and　machine　learning　algorithms　identified　BPI,　GPHA2,　KIR3DL3,　MASP1,　RASGRP1,　and　SEMA4A　as　potential　prognostic　genes.　A　random　survival　forest　(RSF)　model　was　developed　and　subsequently　validated,　alongside　the　creation　of　a　prognostic　nomogram　to　assess　gene　diagnostic　performance.　Further,　epithelial　cells,　fibroblasts,　and　T　cells　were　recognized　as　the　primary　cell　types　through　which　RASGRP1　exerts　its　effects.　Finally,　an　analysis　of　the　immune　microenvironment　and　drug　predictions　provided　fresh　insights　for　the　treatment　of　TNBC.