Abstract Background Bladder cancer (BLCA) is one of the most frequently-diagnosed tumors globally. Disulfidptosis represents a critical framework for cell death mechanism in cancer therapy. Our study constructed a predictive model utilizing disulfidptosis-related lncRNAs (DRLs) to provide value in evaluating diagnosis, drug sensibility, and prognosis of BLCA patients. Methods The study data of BLCA patients retrieved from TCGA-BLCA database. Cox and LASSO regression analysis were used to identify DRLs. Kaplan–Meier survival analysis, ROC curve, and nomograms were constructed to assess and forecast survival events. GSEA were performed to illustrate relevant enrichments results. Tumor mutation burden (TMB), immune status, and drug sensitivity were assessed. Muscle invasive bladder cancer (MIBC) tumor and tumor-adjacent normal tissues samples were collected in our department to validate the DRLs expression levels by RT-PCR. Results Overall, nine DRLs (AL590428.1, LSAMP-AS1, LINC01184, LINC-PINT, AC023825.2, AC010331.1, AC009716.1, AC104785.1, AC008764.6) were identified. These DRLs were used to calculate risk scores and create a prognostic model. ROC revealed higher diagnostic efficiency of the model than other clinical characteristics. Nomogram was constructed using the risk scores, age, and tumor stage, which showed excellent predictive power and was verified by calibration graph. BLCA patients were further classified into high-risk group and low-risk group using median risk score as the cut-off value. The high-risk group showed lesser TMB levels and developed worse prognosis. GSEA of the high-risk group identified pathways associated with BLCA progression such as WNT signaling pathway. Immune cells including CD4+ and CD8+ T cells, and immune-related function like T cell co-stimulation also showed remarkable differences between two risk groups. Furthermore, IC50 values of twelve drugs such as Sorafenib, Nilotinib, and Navitoclax were significantly higher in the high-risk group. RT-PCR results revealed that 9 DRLs expression levels were statistically significant between tumor tissues samples and tumor-adjacent normal tissues samples. The expression trends of these DRLs in clinical tissues samples were the same as the findings in TCGA dataset. Conclusion Based on this study, it would be advisable to identify the key DRLs with potential prognostic value in BLCA to enhance the evaluation of clinical outcomes in this context.