This study introduces an innovative supplier evaluation approach for footwear manufacturing by integrating the SCOR 4.0 model with machine learning techniques. The SCOR 4.0 model, combined with the Best Worst Method (BWM), provides a structured framework for evaluating suppliers across various dimensions. A Random Forest (RF) machine learning model is then used to classify and rank suppliers based on performance ratings. The results show that the RF algorithm effectively identifies suitable suppliers, with lower rejection scores indicating superior performance. This integration enhances supplier evaluation processes and offers valuable insights for supply chain management in the footwear industry. Key Words: Supplier evaluation, SCOR 4.0 model, Best-worst method (BWM), Random Forest (RF), Supplier Rejection score