As wind energy becomes increasingly vital to the global renewable energy portfolio, ensuring the reliability of wind turbines is essential. Failures in components such as bearings, blades, and gearboxes not only disrupt operations but also escalate maintenance costs and reduce turbine lifespan. This thesis proposes a hybrid risk analysis framework that integrates traditional engineering methods with machine learning (ML) algorithms to support predictive maintenance in wind turbines. A qualitative risk matrix was developed to assess failure modes based on likelihood and impact, identifying bearings, blades, and corrosion-prone components as high-risk. For quantitative analysis, real-world SCADA data from two sites (spanning 89 years of cumulative operation) was used to train five ML models: Random Forest, Logistic Regression, Gaussian Naive Bayes, Decision Tree, and Hidden Markov Models. While these models achieved high recall for normal operations (e.g., Random Forest reached 96% for Class 0), they struggled to detect anomalies due to class imbalance and sensor variability—most notably, Hidden Markov Models failed to identify any faults. To address these challenges, the study recommends the use of SMOTE for class balancing, model retraining to handle temporal drift, and enhanced labelling techniques for nuanced fault detection. Component-specific strategies—such as UAV-based blade inspections and slip ring temperature tracking are proposed. Additionally, a digital twin framework is introduced to combine real-time SCADA monitoring with predictive modelling. Overall, this work demonstrates the value of combining classical risk assessment with intelligent analytics, offering a scalable solution for improving wind turbine reliability and maintenance efficiency.