This study proposes a novel approach to assessing supplier disruption risks in the context of global supply chains, which are increasingly complex and uncertain. The approach combines the Pythagorean Fuzzy Step-wise Weight Assessment Ratio Analysis (PF-SWARA) with the Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS). By identifying eight key risk factors through literature review and expert consultation, the PF-SWARA method quantifies the significance of these risks, while the PF-TOPSIS technique calculates each supplier's risk score, facilitating prioritization for targeted improvement. The study finds that "natural disasters and geopolitical risks," "financial instability," and "delivery delays" are the top three critical disruption risk factors. The findings provide practical recommendations for improving supplier performance and enhancing supply chain resilience. The study also highlights the importance of expert opinions and the integration of Pythagorean fuzzy theory to address uncertainties in the assessment process.This study proposes a novel approach to assessing supplier disruption risks in the context of global supply chains, which are increasingly complex and uncertain. The approach combines the Pythagorean Fuzzy Step-wise Weight Assessment Ratio Analysis (PF-SWARA) with the Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS). By identifying eight key risk factors through literature review and expert consultation, the PF-SWARA method quantifies the significance of these risks, while the PF-TOPSIS technique calculates each supplier's risk score, facilitating prioritization for targeted improvement. The study finds that "natural disasters and geopolitical risks," "financial instability," and "delivery delays" are the top three critical disruption risk factors. The findings provide practical recommendations for improving supplier performance and enhancing supply chain resilience. The study also highlights the importance of expert opinions and the integration of Pythagorean fuzzy theory to address uncertainties in the assessment process.