With the growing adoption of Artificial Intelligence (AI) in supply chains, identifying the factors that enable its effective implementation has become a critical issue in sustainable logistics. Despite AI’s significant potential to improve the economic, social, and environmental dimensions of logistics, a comprehensive framework for explaining the factors influencing its deployment in the petrochemical industry remains limited. This study aims to identify and model the AI enablers that contribute to sustainable logistics performance in the petrochemical supply chain. The research adopts an applied-developmental orientation and employs a mixed-methods approach. In the first phase, a systematic literature review was conducted on studies published between 2008 and 2025. As a result, 57 AI enablers were identified and classified into 11 components across three dimensions—technology, organization, and environment—based on the Technology–Organization–Environment (TOE) framework. Subsequently, the identified enablers were validated and localized using the Fuzzy Delphi method, through which five enablers were excluded due to insufficient expert consensus. Finally, the proposed model was assessed using Partial Least Squares Confirmatory Factor Analysis (PLS-CFA). The findings indicate that data infrastructure and governance, organizational capabilities including human capital, organizational culture, and data-driven strategy, as well as institutional conditions and the technological ecosystem, constitute the most critical prerequisites for the successful implementation of AI in enhancing sustainable logistics performance within the petrochemical industry. The proposed model provides a practical framework for designing digital transformation roadmaps and advancing sustainable logistics initiatives.