This study proposes a pioneering approach to the operation and maintenance (O&M) of floating offshore wind turbine (FOWT) infrastructure on MODENERLANDS through the application of digital twins, boosted by advanced Artificial Intelligence (AI) techniques. The escalating challenges of maintaining FOWT structures, exacerbated by harsh marine environments and dynamic forces, demands a shift towards more intelligent and predictive O&M strategies. AI-enhanced digital twins are used, creating a dynamic and responsive model that copies the real-time state of FOWT infrastructure, thereby facilitating a proactive approach to O&M. By synthesising real-time data acquisition, advanced analytics, and predictive modelling, the AI-enhanced digital twins offer a substantial improvement over traditional corrective maintenance method. The digital twins allow for the identification of potential failure modes, prediction of deterioration progression, and recommendation of timely maintenance actions. The case study focuses on the MODENERLANDS project, employing the DTU 10MW reference turbine as a prototype to demonstrate the effectiveness of digital twins in optimizing the lifecycle management of FOWT infrastructure. Multi-physics simulations, informed by site-specific environmental data and material test results, are utilised to predict structural responses and assess corrosion fatigue (CF) deterioration states. The integration of these simulations with a probabilistic CF model enables a comprehensive evaluation of the structural health of FOWT towers and supports the development of refined adaptive control strategies aimed at minimising O&M costs while maximising operational durability and reliability. The research output not only highlights the potential for significant advancements in FOWT O&M but also sets a new benchmark for intelligent infrastructure management in the civil engineering sector.