In a landscape characterized by multifaceted uncertainties, borrowers face challenges in meeting their loan obligations while financial institutions grapple with accurately assessing credit risk. To address these challenges, financial institutions are increasingly integrating environmental, social and governance (ESG) criteria into their risk management strategies. Concurrently, advancements in artificial intelligence (AI) and machine learning (ML) are revolutionizing the current credit risk models. By utilizing bank-level data for the top twenty (20) European banks financing maritime borrowers, for the period 2010–2022, we investigate the significance of integrating ESG criteria in predicting shipping loan defaults. By applying the ML approach, our results demonstrate that the integration of the ESG criteria improves the predictive capability of the ML models concerning maritime loan defaults. Notably, among the ESG criteria, the combined ESG score offers the best predictive accuracy, followed by the environmental, governance and social criteria.