In the modern era, uncertainty is a common feature of modeling techniques for designing sustainable supply chains. The increasing severity of environmental issues necessitates the integration of sustainable production in supply chain management. The present study aims to develop mathematical models and intelligent sustainable supply chains with uncertain parameters and algorithms. The goal is to design a sustainable and eco-friendly model that minimizes environmental contaminants and system costs. This descriptive-analytical study employs a novel hybrid technique to manage the uncertainty associated with the model parameters, research problems, and problem complexity, and tackle large-scale problems. The automotive industry was selected to implement the mathematical model. These combined techniques consider the disruption-induced capacity reduction and the uncertainties surrounding shipping costs and demand. Results suggest that hybrid models and techniques are efficient in solving large-scale problems and delivering high-quality processing. Further, the findings show that heuristic solutions can significantly reduce computation time for larger problems.