This study utilizes the Analytic Hierarchy Process (AHP) to identify the most influential factors derived from a rigorously validated SWOT analysis, specifically aimed at enhancing machine learning models within the credit scoring field. By employing this integrated approach, AHP systematically assigns weights to the factors within the SWOT matrix, allowing for a thorough evaluation of their significance and impact on improving credit scoring through machine learning models. In doing so, this research makes a significant contribution to the advancement and refinement of these models. By shedding light on critical factors and areas ripe for improvement, the study offers valuable insights for both practitioners and researchers, highlighting key considerations for developing models in credit risk assessment.