Digital twins are increasingly used as operational interfaces that connect physical assets, engineering models, and instantaneous data. However, their environmental and safety benefits remain fragmented in oil and gas and construction research. This review develops an integrative framework for Saudi Arabian facilities and projects, where rapid infrastructure delivery, challenging climate, and high-risk operations require proactive risk management. The review systematically synthesizes peer-reviewed literature from 2020 to 2025, using Scopus, Web of Science, IEEE Xplore, and other databases. It compares digital-twin architectures, sensing strategies, predictive analytics, construction safety applications, process-integrity uses, environmental monitoring, and Saudi-specific implementation factors. Evidence shows that digital twins can improve risk visibility by integrating asset context with live data, support scenario-based decisions before intervention, and link environmental indicators to work controls. Realizing these benefits depends on synchronization quality, model validation, data interoperability, cybersecurity, human oversight, and lifecycle governance. The literature is uneven: construction studies often focus on localized safety or monitoring, while oil and gas studies emphasize asset integrity and operational optimization, with few integrated environmental-safety frameworks. This paper proposes a layered, closed-loop framework that integrates physical sensing, BIM and engineering data, physics and AI models, environmental and safety intelligence, predictive decision support, governance, and continuous learning. In Saudi Arabia, the framework is designed to support auditable, human-supervised decisions for sustainable project delivery and resilient operations, rather than serve as an autonomous control system.