Enhancing Offshore Wind Turbine Health Monitoring through a Hybrid Approach of Reduced Order Modelling and Machine Learning The increasing demand for sustainable energy sources has driven the development of offshore wind turbines, which, due to their unique operating environment, require vigilant structural health monitoring. This paper introduces an innovative approach that combines reduced order modelling (ROM) with advanced machine learning (ML) techniques to enhance the effectiveness of monitoring and predictive analysis in offshore wind turbines. The presented methodology integrates the efficiency of ROM in simplifying complex structural systems with the robustness of ML in pattern recognition and predictive analytics, primarily focusing on critical parameters such as fatigue life. The development of a specialized reduced order model tailored for offshore wind turbines is detailed, highlighting its ability to capture essential dynamics while significantly reducing computational overhead. This model serves as the foundation for the subsequent integration of ML algorithms, including neural networks and ensemble methods, to process and interpret data from the ROM. The combination of ROM and ML enables the system to effectively learn from historical data, thereby improving its predictive accuracy in terms of the structural health and fatigue life of the turbine. A comprehensive analysis of real-world data from operational offshore wind turbines is carried out to validate the model's performance. The findings indicate a significant improvement in the early detection of potential structural issues and accurate fatigue prediction, which contributes to extending the lifespan of these turbines and reducing maintenance costs. This paper makes a contribution to the field by providing a scalable and efficient solution for offshore wind turbine health monitoring, supporting the trend towards more reliable, safe, and cost-effective renewable energy production. The adaptability of the proposed model also suggests its potential for broader application in various structural health monitoring scenarios, representing a notable advancement in the integration of machine learning into engineering applications.