The number of installed Floating Offshore Wind Turbines (FOWTs) has doubled since 2017, quadrupling the total installed capacity, and is expected to increase significantly over the next decade. Consequently, there is a growing consideration towards the main challenges for FOWT projects: monitoring the system's integrity, extending the lifespan of the components, and maintaining FOWTs safely at scale. Effectively and efficiently addressing these challenges would unlock the wide-scale deployment of FOWTs. In this work, we focus on one of the most critical components of the FOWTs, the Mooring Lines (MoLs), which are responsible for fixing the structure to the seabed. The primary mechanical failure mechanisms in MoLs are extreme load and fatigue, both of which are functions of the axial tension. An effective solution to detect long term drifts in the mechanical response of the MoLs is to develop a Digital Twin (DT) able to accurately predict the behaviour of the healthy system to compare with the actual one. Authors will leverage operational data collected from the world's first commercial floating wind farm (Hywind Pilot Park1) in 2018, to investigate the effectiveness of the DT for the prediction of the MoL axial tension. The DT will be developed using state-of-the-art data-driven methods, and results based on real operational data will support our proposal.