Predicting Turkey's Sustainable Development Performance Using Machine Learning

ABSTRACT This study examines how machine learning techniques can be used to predict Turkey's national sustainability performance in a comprehensive and data‐driven manner. Sustainable development is a multidimensional concept shaped by economic, social, environmental, and technological factors, making its assessment challenging through traditional analytical methods. Using official national and international data covering the period 2000–2022, this study develops predictive models based on key sustainability indicators. Several machine learning algorithms are applied and compared to evaluate their ability to capture complex relationships among sustainability dimensions. The results show that machine learning models can successfully forecast Turkey's sustainability performance with high accuracy, highlighting the importance of technological capability, economic structure, social development, and environmental conditions. The findings demonstrate the potential of machine learning as a decision‐support tool for sustainability monitoring and policy design. This study contributes to the literature by providing a replicable framework for national‐level sustainability assessment and offers practical insights for policymakers seeking evidence‐based strategies to support sustainable development goals (SDGs). From a policy perspective, the results provide concrete guidance for Turkey and similar developing countries by highlighting priority areas such as digital infrastructure, innovation capacity, female employment, and renewable energy policies to improve long‐term sustainability performance.