Disulfidptosis and ferroptosis, recently identified patterns of programmed cell death, play pivotal roles in the progression of breast cancer. This study aimed to explore the potential of disulfidptosis and ferroptosis in the prognostic stratification of Breast Cancer. Correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, univariate and multivariate Cox regression analyses were performed to identify the core long non-coding RNAs associated with disulfidptosis and ferroptosis. A risk signature and a prognostic nomogram were constructed based on these findings. Additionally, investigations concerning functional pathways, mutation landscapes, immune infiltration, and drug sensitivity were conducted in different risk stratification groups. Machine learning analyses revealed a risk signature comprising seven long non-coding RNAs closely associated with disulfidptosis and ferroptosis. Validated in two datasets, breast cancer patients with high-risk scores exhibited a poorer prognosis. The prognostic nomogram, integrating the risk signature with age and TNM stage, demonstrated a favorable predictive capability for survival outcomes. Furthermore, the high-risk group showed a higher tumor mutation burden compared to the low-risk group, which was also characterized by immune suppression and sensitivity to cisplatin, lapatinib and olaparib. Our study highlights the crucial role of disulfidptosis and ferroptosis in guiding clinical decision-making for patients with breast cancer, which also characterizes the intricate landscape of breast cancer and deepens our understanding of tumor heterogeneity.