​ Abstract: Artificial Intelligence (AI) and Digital Twin (DT) technologies are increasingly converging to support the transition toward sustainable and resilient manufacturing. This study conducts a comprehensive analysis of 118 high-impact indexed publications (2013–2025) using bibliometric, thematic clustering, and content analysis methods to map the evolution, applications, and gaps at the intersection of AI-DT and sustainable manufacturing. The results identify five core thematic clusters reflecting a shift from process-level optimization to system-wide sustainability: i) Environmental Intelligence and Energy Efficiency, ii) Industrial Cyber-Physical Infrastructure, iii) Intelligent Human–Machine Collaboration, iv) Smart Sustainable Manufacturing, and v) Automation and Predictive Maintenance. The analysis reveals that research primarily targets energy/resource efficiency, smart decision-making, and Industry 5.0 human-centric systems, with Machine Learning (29.2%), Cyber-Physical Systems (17.7%), and IoT (14.4%) as dominant associated enabling technologies. Despite notable progress, critical gaps persist in cybersecurity (3.1%), explainable AI, and the integration of lifecycle and circular economy principles. This study highlights the urgent need for interoperable, secure, and human-aware AI-DT architectures and proposes a capability framework to guide future developments. The insights provide a strategic roadmap for researchers and practitioners aiming to unlock the full potential of AI and digital twins in driving sustainable industrial transformation.

Keywords: Sustainable development, Industry, Innovation and infrastructure, Sustainable production, Artificial intelligence, Digital twins, Responsible consumption and production.

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