Digital twin technology — the creation and continuous synchronisation of a virtual replica of a physical asset using real-time sensor data, computational models, and machine learning algorithms — represents the most significant paradigm shift in infrastructure asset management since the introduction of pavement management systems in the 1980s. This paper presents the design, implementation, and performance evaluation of the EAC-DT, a scalable Digital Twin Framework for monitoring critical road and bridge infrastructure across the East African Community highway network. The EAC-DT integrates five data acquisition modalities — IoT sensor networks (accelerometers, strain gauges, acoustic emission sensors, piezometers), unmanned aerial vehicle (UAV) photogrammetry, satellite interferometric synthetic aperture radar (InSAR), weigh-in-motion (WIM) stations, and mobile road condition survey vehicles — into a unified cloud-based data lake, feeding a core digital twin engine that combines finite element model updating, BIM-GIS integration, and ensemble machine learning for real-time condition assessment and predictive maintenance. The framework was piloted on a 180 km segment of the Kenya A104 corridor (Nairobi–Mombasa) encompassing 14 bridges and 12 pavement management sections, and subsequently validated on the Uganda A109 (Kampala–Malaba, 130 km) and Tanzania T1 (Dar es Salaam–Morogoro, 95 km) corridors. Across the three pilot corridors, the EAC-DT achieved IRI prediction accuracy of RMSE = 0.21 m/km (compared to 0.68 m/km for conventional visual surveys), bridge structural health index prediction R² = 0.94, and an average 2.4-day advance warning of pavement distress events with 87% detection rate. Life-cycle cost analysis demonstrates that digital twin-enabled asset manageme