<p>Achieving sustainable development has become increasingly urgent under mounting global pressures, with artificial intelligence (AI) drawing increasing attention for its dual potential to advance or hinder progress. This study addresses limited empirical evidence by examining AI’s relationship with sustainable development across 25 EU member states from 2000 to 2021. Using a bootstrap-based simultaneous quantile regression framework, the marginal effects of AI were estimated alongside nine macro-structural variables, covering economic, institutional, social, and environmental dimensions. This approach enables distribution-sensitive inference that captures heterogeneity in performance levels across countries. Results reveal substantial heterogeneity across sustainability performance levels. Artificial intelligence significantly improves sustainability outcomes in lower-performing countries (Q10: + 0.102, <i>p</i> &lt; <i>0.01</i>) but its effect weakens at higher quantiles. In contrast, renewable energy becomes particularly beneficial in advanced contexts (Q90: + 0.106, <i>p</i> &lt; <i>0.01</i>). Economic development shows strong gains at early stages but diminishing influence at higher sustainability levels, while trade openness and urbanization display context-dependent effects across the distribution. Governance quality becomes decisive in high-performing economies, whereas the impacts of natural resource rents and female labor force participation vary across performance levels. These findings highlight the asymmetric nature of AI’s contribution, the growing importance of renewable energy in advanced contexts, and the need for tailored EU policies that align digital transformation with environmental sustainability. Overall, the study provides the first EU-focused, quantile-based empirical assessment of AI’s influence on sustainable development and contributes a multidimensional analytical framework to the literature, with policy implications discussed.</p>