Artificial Intelligence and Digital Transformation for Sustainable Engineering Systems: A Review of Leadership, Circular Economy, Digital Twins, and Machine Learning

Abstract

The rapid advancement of Artificial Intelligence (AI) and digital transformation technologies is fundamentally reshaping sustainable engineering systems across manufacturing, supply chain management, energy, infrastructure, and industrial operations. Modern engineering systems increasingly integrate intelligent technologies, including machine learning, digital twins, blockchain, cloud computing, and data analytics, to improve operational efficiency, resource utilization, decisionmaking, and environmental sustainability. Simultaneously, organizational leadership, circular economy principles, and effective project management have emerged as critical enablers for the successful adoption of digital transformation initiatives. This review presents a comprehensive overview of recent developments in AI-driven digital transformation for sustainable engineering systems by synthesizing research on digital leadership, circular economy, blockchain-enabled supply chains, machine learning, digital twin technologies, and intelligent engineering applications. The review first examines the role of leadership and organizational transformation in promoting sustainability and digital innovation. It then discusses the contributions of machine learning algorithms, including Random Forest and Gradient Boosting, toward intelligent decision support and predictive analytics. Furthermore, recent advances in digital twin technology and AI-assisted engineering applications are reviewed using reservoir characterization as an illustrative case study of intelligent engineering systems. Finally, current research challenges, including data integration, scalability, sustainability, interoperability, cybersecurity, and organizational readiness, are analyzed, followed by future research directions emphasizing explainable AI, human-centered digital transformation, and integrated intelligent engineering ecosystems. By consolidating diverse research areas into a unified framework, this review highlights how AI-enabled digital transformation can support resilient, efficient, and sustainable engineering systems while providing guidance for future interdisciplinary research and industrial implementation.