Classifying Global Economies Based on Sustainable Development Goals: A Data‐Driven Clustering Approach

ABSTRACT The concept of sustainable development gained global prominence after its widespread recognition and the establishment of the United Nations' 2015 sustainable development goals (SDGs). These goals aim to tackle the issues of poverty, inequality, and environmental sustainability by 2030. This study utilizes machine learning clustering techniques, namely, Grey Relational Analysis and K‐means clustering, to classify 167 countries based on their SDG performance between 2000 and 2024. The analysis identifies four clusters, with Cluster 3 demonstrating the strongest overall performance. An ordered logit model is used to assess which SDGs mostly influence cluster membership, revealing that tailored policy interventions are crucial for addressing different countries' development challenges. This approach offers a dynamic perspective on the global SDG progress and enables more targeted and effective policy‐making toward attaining the 2030 Agenda.