This tries to review challenges and opportunities in AI-driven supply chain sustainability comprehensively. Thus, qualitative research aims to study the implications and possibilities of Artificial Intelligence (AI) technologies associated with supply chain sustainability (SCS). Sustainability is one of the most crucial components of modern ethical and social practices due to environmental problems, such as global warming and resource depletion. At the same time, SCS is applied to attain economic sustainability and maintain competitive business models with environmental and social responsibility. The AI technologies can project demands, record optimization, predictive maintenance, and supplier risk management to facilitate supply chain operations. So, AI significantly optimizes supply chain operations by facilitating decision-making processes and saving suppliers’ and customers’ time and money. However, the trained AI model could be unfair or unethical and may lack transparency and accountability, thereby raising the privacy issues of both suppliers and customers. The integration of AI technologies into SCS also presents several opportunities for organizations. For instance, demand forecasting may be enhanced with AI by using historical data to predict trends in business, which will affect inventory handling. It will allow the avoidance of overstock situations and stockout shelves, subsequently saving costs and inventory wastage for organizations. Another important aspect is that the production demand fluctuations for a particular product could be predicted, and adequate inventory levels could be maintained. It is also helpful for better management of sourcing and supplier relations. It will help organizations to pursue ‘green’ sourcing practices, rely on more ethical sourcing solutions, and optimize their supply chains.