Background: Durvalumab, an anti-PD-L1 antibody, has established clinical benefit across multiple NSCLC treatment settings. However, survival outcomes vary substantially across these settings. Characterizing setting-specific survival profiles is essential for informed clinical decision-making. Methods: A systematic review identified 39 durvalumab NSCLC trials (2015–2025). Kaplan-Meier curves were digitized and patient data reconstructed via IPDfromKM. Time-to-event models were developed for OS and PFS across first-line, second/third-line, and consolidation settings each, with covariates explored via forward inclusion and validated by visual predictive checks. Results: Log-logistic and Log-normal models provided the best fit for OS and PFS, respectively. Estimated median OS was 12, 9, and 39 months and median PFS was 7, 3, and 15 months for first-line, second/third-line, and consolidation settings, consistent with results from previous pivotal trials (POSEIDON, ATLANTIC, PACIFIC, etc.) Covariate analyses identified previous treatment, stage, age, and monotherapy status as significant predictors of survival. Visual predictive checks confirmed adequate model performance. Conclusion: Parametric TTE modeling of reconstructed trial data enabled survival estimation across three NSCLC settings, integrating evidence from multiple trials to characterize setting-specific survival profiles of durvalumab and support clinical decision-making.