The paper explores how AI can make logistics both greener and more efficient. Using models like XGBoost, neural networks, and clustering algorithms, the study focuses on three core areas: predicting demand more accurately, optimizing delivery routes to reduce travel time and emissions, and minimizing fuel consumption. The models are evaluated with standard metrics (MAE, MSE, R²) and designed for real-world deployment through cloud, edge, or hybrid systems. The findings show AI can significantly cut costs and carbon footprints while improving service efficiency, making it a practical tool for eco-friendly supply chains. The paper also highlights future opportunities such as integrating blockchain, quantum computing, and common sustainability metrics to further strengthen AI-driven green logistics.
