Breast cancer (BC) remains one of the most pervasive and complex malignancies. PANoptosis represents a recently identified cellular mechanism leading to programmed cell death. However, the prognostic implications and influence on the immune microenvironment of BC pertaining to PANoptosis-related genes (PRGs) remain significantly understudied. We conducted differential expression analysis to identify prognostic-Related PRGs by the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Next, we identified the PANoptosis-related molecular subtype using the consensus clustering analysis, and constructed and validated the PANoptosis-related prognostic signature using LASSO and Cox regression analyses. ROC curves were employed to assess the performance of the signatures. Furthermore, drug sensitivity between low- and high-risk group were analysis. Finally, we conducted RT-qPCR to assess the gene expression levels involved in this signature. We categorized BC patients into 2 distinct molecular clusters based on PRGs and identified differentially expressed genes associated with prognosis. Subsequently, BC patients were then divided into 2 gene clusters. The identified PRGs molecular clusters and gene clusters demonstrated association with patient survival, immune system functions, and biological processes and pathways of BC. A prognostic signature comprising 5 genes was established, and BC patients were classified into low- and high-risk groups based on the risk scores. The ROC curves demonstrated that those in the low-risk category exhibited notably extended survival compared to the high-risk group. A nomogram model for patient survival was constructed based on the risk score in conjunction with other clinical features. High-risk group had higher tumor burden mutation, CSC index and lower StomalScore, ImmuneScore, and ESTIMATEScore. Subsequently, we established a correlation between the risk score and drug sensitivity among BC patients. Finally, qRT-PCR results showed that the expression of CXCL1, PIGR, and TNFRSF14 significantly decreased, while CXCL13 and NKAIN were significantly increased in BC tissues. We have developed a molecular clustering and prognostic signature based on PANoptosis to improve the prediction of BC prognosis. This discovery has the potential to not only assist in assessing overall patient prognosis but also to deepen our understanding of the underlying mechanisms of PANoptosis in BC pathogenesis.