<p>Sustainable development remains a global priority under the United Nations’ 2030 Agenda, with SDG 16 emphasizing strong institutions and anti-corruption measures. However, corruption continues to hinder progress, particularly in developing countries with fragile governance systems. This study examines how governance quality moderates the corruption–sustainable development nexus, using data for 64 developing economies from 2002 to 2023. The Method of Moments Quantile Regression (MMQR) is employed to capture heterogeneous effects across the distribution of sustainable-development performance. Two complementary corruption measures, the Bayesian Corruption Index (BCI) and the Corruption Perceptions Index (CPI), are incorporated alongside an interaction term between corruption and governance. Unlike previous studies that rely primarily on mean-based estimators, this research applies a quantile-based moderation framework to uncover distributional heterogeneity in how corruption affects sustainable development. The study also advances the institutional literature by conceptualizing governance as a conditional transmission mechanism, allowing the moderating role of governance to vary across development levels—a dimension largely overlooked in prior work. Furthermore, the use of dual corruption indicators (BCI and CPI) provides a more rigorous and robust assessment of corruption’s impact, strengthening the empirical validity of the findings. Our results show that corruption exerts a detrimental effect on sustainable development across most quantiles, while governance quality significantly mitigates this negative impact. These contributions position the study as an extension of existing theories on corruption, governance, and sustainable development, offering deeper insights for policymakers aiming to design context-specific institutional reforms.</p>