The increasing complexity of modern supply chains, particularly in the automotive sector, demands intelligent systems that ensure efficiency, reliability, and adaptability. However, despite advancements in machine learning, many current approaches struggle to generalize effectively in dynamic, data-scarce environments. To address this challenge, we propose a novel stacking meta-learning approach that combines the adaptability of meta-learning with the domain-specific knowledge from expert systems, redefining predictive modeling and strategic decision-making. Our methodology integrates Model-Agnostic Meta-Learning (MAML) with a dynamic stacking prediction framework, combining outputs from specialized base models to improve accuracy and robustness. This hybrid system effectively handles unseen data, distributional shifts, and generalization with limited labeled data. Comprehensive evaluations on diverse prediction tasks demonstrate a 15% improvement in \(R^2\) scores and a 10% reduction in RMSE compared to state-of-the-art models. By synthesizing domain expertise with advanced machine learning, our work sets a new benchmark for predictive modeling in dynamic logistics and offers broad applicability to fields such as predictive maintenance, financial forecasting, and healthcare. This research is applied within an automotive company’s outbound logistics supply chain management system, enhancing quality measurement at every transfer point from the plant to the dealer. The system provides transparent damage assessment and evaluation, focusing on preventive remedial measures in the event of recurring damage patterns.