The Digital Twin Road initiative aims to improve road infrastructure monitoring and maintenance through real-time data integration, computational modelling, and predictive analytics. However, the reliability of such digital twins is significantly affected by uncertainties in both material properties and sensor data. This paper provides a comprehensive evaluation of uncertainty sources within the CRC/TRR 339–Digital Twin Road project. Material-related uncertainties stem from the intrinsic variability of asphalt, concrete, and soil due to production methods, environmental exposure, and construction practices. These include aleatoric uncertainties from natural variability and epistemic uncertainties from knowledge gaps. Sensor-related uncertainties arise from limitations in sensor technology, calibration, environmental influences, and data processing algorithms. Detailed case studies, including weigh-in-motion systems, drone-mounted laser scanning, and smart materials such as mineral-impregnated carbon-fibre (MCF) reinforced low-clinker concrete, illustrate how uncertainties accumulate and propagate across the Digital Twin Road framework. The classification of uncertainty into aleatoric and epistemic categories is discussed. Understanding these uncertainty sources is essential for improving the predictive accuracy and ensuring a robust representation of real-world road systems.