Researchers and practitioners are stressing sustainability considerations considerably more due to the disruptions, hazards, and problems facing supply chain networks observed during the past decades. Sustainable supply chains balance economic, social, and environmental performance—such as employee rights assurance, legal workplace practices, carbon emissions reduction, resource efficiency, and waste generation management. Computational intelligence techniques can be beneficial for making supply chains sustainable enough to achieve their long-term goals. Businesses that use technologies like tracing and mapping, automation and robotics, and transportation innovations like electric automobiles may gain transparency, energy efficiency, waste reduction, and other benefits. Machine learning (ML) and artificial intelligence (AI) are gaining popularity in the supply chain industry faster than ever. These technologies, which employ scenario analysis and numerical analytics, enable new automation capabilities that aid planning operations, predictive maintenance, demand forecasting, synchro modality, and collaborative shipping. As a result, big data analytics, AI, ML, robotics, and other supply chain quantitative skills and capabilities may substantially reduce error rates, minimize operational expenses, and improve supply chain flow. This chapter aims to provide an outline of the computational tools, techniques, and methods that have been used in the literature to establish sustainable logistics, procurement, inventory management, production, scheduling, transportation, and overall supply chain networks, as well as to make recommendations for future research.