Cold chain logistics are vital for uninterrupted supplies of perishable essentials such as foods and pharmaceuticals across the European Union and its borders. The assurance of the availability and safety of these goods, however, is energy-intensive and emission-heavy. It contributes significantly to greenhouse gas outputs due to refrigeration demands and product spoilage. In parallel, EU climate policies are placing pressure on logistics providers because of their environmental impact. This literature review explores how artificial intelligence (AI) can contribute to emissions mitigation in this context. Particularly, we examine the potential and role of AI in vehicle routing, autonomous transport, traffic forecasting, predictive maintenance, demand forecasting, dynamic pricing, and decision support. We outline how these means of optimisation are interconnected and how an enhancement in one may improve the efficiency of the others. Our findings suggest that AI-driven strategies outperform conventional solutions in terms of time-efficiency, cost-efficiency and simplicity. We hope our research will confirm the faith of policy makers in AI as a major driver of emission reduction.