<![CDATA[Sustainable development is a global challenge which requires an innovative approach merging environmental science, economics, policy-making, and artificial intelligence. The data-driven approach using intelligent methodologies is valuable for evaluating and mitigating environmental impacts. This study exploits data from different sources and machine learning methods to analyze key sustainability indicators, focusing on CO2 emissions, ecological footprint, and load capacity factor. The analysis emphasizes advanced feature selection techniques and predictive modelling to identify the most significant economic, industrial, agricultural, and environmental factors that affect sustainability. Comparative analysis shows differences between the importance of indicators established through expert-driven decisions across various scientific fields and AI-driven assessments. The research attempts to solve the problem following a multi-step process: (1) clustering of countries based on environmental indicators to identify patterns and classify according to similar performance; (2) evaluation of the socio-economic and environmental factors’ impact on CO2 emissions using machine learning; (3) predicting future trends in emissions and sustainability metrics through high-level artificial intelligence techniques such as Hidden Markov models. This study will potentially serve policymakers, enabling data-driven decision-making to promote sustainable development efforts. The results demonstrate the value of interdisciplinary approaches to deal with sustainability challenges and to stimulate a balanced path toward economic growth and environmental protection.]]>
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