<![CDATA[Digital twin technology has emerged as a transformative paradigm in information systems (IS), enabling real-time virtual simulation of physical processes to support adaptive decision-making. This study presents a systematic literature review of 25 peer-reviewed articles published between 2021 and 2025, examining how digital twins integrate with IS architectures, simulation engines, and AI-driven analytics to produce actionable insights in real time. The review covers applications in manufacturing, healthcare, transportation, agriculture, enterprise business intelligence, and library information systems. Key findings indicate that digital twins provide significant advantages in decision-making speed, predictive accuracy, and system optimization, though challenges remain in data interoperability, model validation, computational cost, and cybersecurity. The proposed research framework maps the end-to-end flow from physical data acquisition through digital twin simulation to real-time decision support, providing a conceptual foundation for future implementations. This paper contributes a structured overview of the state of the art and identifies priority areas for continued research and practice.]]>
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