This study investigates advanced diagnostic techniques for predicting bearing faults in industrial turbogenerators using Artificial Neural Networks (ANNs) and Support Vector Regression (SVR). Real-world data were collected from a 58 MW steam-driven turbogenerator over a two-year period, including vibration and rotational speed measurements, to evaluate the reliability of predictive maintenance strategies. A threshold-based monitoring approach was first applied to classify machine health using predefined alarm and trip limits for speed (3003–3005 rpm) and vibration (27–29 mm/s). Machine learning techniques were then implemented, including SVR models for regression analysis and ANN models trained using Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) algorithms. The results indicated that threshold-based monitoring successfully identified abnormal operating conditions, with multiple instances of excessive vibration and speed exceeding critical limits. However, linear SVR models demonstrated poor predictive performance, with negative R² values, highlighting the nonlinear nature of the system. In contrast, ANN models achieved high prediction accuracy, with Bayesian Regularization providing the most stable and robust performance across datasets. These findings confirm the effectiveness of hybrid diagnostic frameworks that integrate physical threshold monitoring with AI-based models for reliable predictive maintenance in industrial turbogenerator systems.