Sustainable digital twins (DTs) are becoming indispensable in civil engineering for managing the lifecycle of offshore wind turbines (OWTs), which are critical for exploiting wind resources but prone to deterioration, particularly corrosion fatigue (C-F) in welded connections. Ensuring the long-term structural integrity of OWTs under site-specific conditions is vital for sustainable operation. Advanced monitoring systems like supervisory control and data acquisition (SCADA) and structural health monitoring (SHM) generate extensive data throughout the OWTs’ service life. However, this data is often spatially sparse and insufficient to cover all critical connections across a wind farm. This study presents a sustainable digital twin approach to manage deteriorating OWT structures, focusing on the welded connections. The approach integrates SCADA and SHM data to enhance physical OWTs with predictive capabilities, promoting sustainable lifecycle management. A case study conducted on a typical offshore wind farm in China involves both a specially monitored OWT and additional OWTs with fewer monitoring devices. The approach begins by pre-processing SCADA and SHM data through cleaning, normalizing, and synchronizing. Multi-physics simulations tailored to the local conditions of the monitored OWT train a correlation model that maps structural responses to loading histories, enabling virtual sensing. Additionally, a correlation model informed by these simulations and the spatial relationships between the monitored and other OWTs is developed to predict loading histories across the wind farm, even with limited SHM data. By combining these reproduced loadings with climate data, the sustainable digital twin predicts C-F deterioration in welded connections, facilitating intelligent maintenance throughout the OWTs’ lifecycle. This research highlights the potential of sustainable digital twins to improve structural condition assessments and maintenance strategies, contributing to the extended lifecycle and operational sustainability of OWT structures.