Hydrogen sulfide (H2S) is a vital gasotransmitter involved in breast cancer (BC) pathogenesis. This study aims to employ hydrogen sulfide-related genes (HSRGs) for molecular classification of BC and, accordingly, to establish a robust prognostic risk signature. Transcriptomic, clinical, and mutational data of BC patients were collected from the cancer genome atlas and gene expression omnibus databases. Prognostic relevance was evaluated using Cox regression analysis, while consensus clustering analysis was employed for molecular subtyping. Gene expression profiles, prognosis, immune infiltration patterns, drug sensitivity, and response to immunotherapy were compared between subtypes. Multiple-gene prognostic features were developed and assessed along with a nomogram. The gene expression was validated in clinical samples using quantitative polymerase chain reaction. Among 282 HSRGs, 46 exhibited significant correlations with BC prognosis. Consensus clustering identified 2 distinct molecular subtypes (C1 and C2). C1 displayed significantly improved prognosis compared to C2, accompanied by increased infiltration of B cells, T cells, monocytes, and mast cells but decreased macrophage infiltration. Moreover, C1 demonstrated higher drug sensitivity and immunotherapeutic response relative to C2. Enrichment analysis revealed suppressed immune-related processes and pathways in C2 while cell cycle regulation and chromosomal processes were significantly activated. Additionally, a risk feature comprising 6 differentially expressed genes between subtypes was constructed; this feature performed well in prognostic prediction. Integration of this feature with other clinical parameters (radiotherapy/chemotherapy status, clinical stage, N stage) into a nomogram further enhanced prognostic accuracy. Clinical samples further validated the high expression of ATP13A5, LRTM2, MAFA, and SPDYC and the low expression of CYP4F12 and TNN in BC. Our findings highlight the clinical relevance of HSRGs in BC, providing a basis for precise molecular classification and prognosis evaluation. The developed risk feature and nomogram offer practical tools for guiding personalized treatment strategies in clinical practice.