The sentinel lymph node (SLN) plays a crucial role in the early treatment of breast cancer. The present study aims to investigate the impact of SLN-associated long noncoding RNAs (lncRNAs) on breast cancer and the influence of molecular subtyping based on related genes on prognosis. To identify SLN-associated lncRNAs, we conducted differential expression analysis using 2 high-throughput sequencing techniques. In addition, ConsensusClusterPlus was employed to establish lncRNA molecular subtypes. Subsequently, comprehensive analysis using LASSO regression was performed to construct an optimal model for predicting breast cancer prognosis. Finally, various functional annotation databases were utilized to elucidate the potential functions of the predictive model. Through differential expression analysis, we identified 14 SLN-associated lncRNAs. These genes primarily influence TNF signaling pathways. Furthermore, we found that lncRNA H19 is a prominent regulatory factor among these 14 gene expressions. By utilizing ConsensusClusterPlus, we successfully stratified the IR samples into 2 distinct subtypes. Through LASSO regression, we established a prognosis model predominantly impacting various immune cells and drug resistance. After verifying 10 pairs of organizations through PCR, we found differences in 6 lncRNAs between the 2 groups of SNLs. At the same time, in the subsequent analysis of immune infiltration and drug targets, it was found that TRPC2 plays a very critical role in breast cancer. Our study highlights the significance of SLN-associated lncRNAs, unveiling the intricate mechanisms underlying the progression of breast cancer. These findings provide novel insights and potential targets for future therapeutic interventions.