Abstract: Material service evaluation is crucial to ensure the safety of major engineering equipment and optimize maintenance strategies. It encompasses performance characterization, failure analysis, and the prediction of the remaining life. However, as service environments in fields, such as aerospace, nuclear energy, and transportation, have become increasingly complex, traditional evaluation methods have limitations. These conventional approaches, which often rely on periodic offline testing and empirical formulas, struggle to meet the modern demands for real-time monitoring, high-fidelity accuracy, and intelligent decision-making. Digital twin technology has emerged as a transformative solution to these problems. By integrating high-fidelity physical models, data-driven approaches, and real-time sensing data, a digital twin can enable dynamic bidirectional mapping between a physical entity and its virtual counterpart. This technology enables the continuous perception of material states and the precise prediction of service performance throughout the lifecycle. In terms of material degradation, the four most prevalent and critical failure modes are corrosion, fatigue, fracture, and wear. However, their occurrence directly threatens the operational safety of large-scale structures. This paper provides a systematic review of recent progress in the application of digital twin technology to evaluate these four typical failure modes. For corrosion evaluation, this study highlights how a digital twin integrates environmental sensor data with electrochemical models to transition from offline analysis to real-time rate prediction. Recent studies on fatigue and fracture have emphasized the integration of multiscale simulation techniques. These approaches combine microscopic crystal plasticity modeling with macroscopic finite element analysis. Consequently, the accumulation of fatigue damage and the propagation behavior of cracks can be tracked with improved reliability and accuracy. For wear evaluation, digital twin technology is increasingly incorporating machine-learning and transfer-learning techniques. These methods can identify the degradation patterns in tools, gears, and other mechanical components under varying operational conditions. Such approaches significantly enhance the adaptability and predictive capability of wear-monitoring systems. In addition to reviewing failure-mode applications, this paper provides a comprehensive discussion of the key enabling technologies required to construct an effective digital twin framework for material service evaluation. Multisource heterogeneous data fusion was identified as the fundamental basis for integrating the experimental measurements, operational monitoring data, and simulation outputs. Multiscale modeling is an essential approach for connecting microscopic damage mechanisms to macroscopic structural responses. Furthermore, real-time data transmission and edge computing technologies were examined. These technologies support low-latency communication and ensure synchronization between physical systems and their digital counterparts. Despite considerable progress in recent years, several technical challenges remain. The integration of high-dimensional heterogeneous data requires further methodological developments. Multiscale simulations often involve high computational costs, which limits their real-time application potential. Additionally, balancing model complexity with real-time performance remains a significant issue in practical engineering deployment. Finally, future development directions for digital twin technology in material service evaluation are discussed. Emerging cloud, edge, and terminal collaborative architectures are expected to enhance computational efficiency and data-processing capability. The integration of uncertainty quantification methods is also emphasized, as it enables probabilistic risk assessment and improves decision reliability. These developments will further promote intelligent and reliable material service evaluation in complex engineering systems.