Predictive maintenance powered by Artificial Intelligence (AI) is transforming asset management in renewable energy systems. This chapter presents a case study at the Saudi Electric Services Polytechnic (SESP), where AI techniques were applied to solar PV inverters and wind turbine trainers. By leveraging condition-monitoring data and maintenance logs, supervised learning models were developed for early fault detection and time-to-failure (TTF) estimation. A Random Forest classifier achieved an F1 score of 0.92 and an area under the ROC curve (AUC) of 0.95 for binary fault detection, while an ANN regressor predicted TTF with a mean absolute error (MAE) of 3.4 days and a root mean squared error (RMSE) of 4.2 days. The framework enabled just-in-time maintenance scheduling, reducing unplanned downtime by 38% and operational costs by 27% compared to conventional approaches. Temporal feature engineering, rolling statistics, and exponentially weighted moving averages captured degradation trends, and governance measures, including cybersecurity and reproducibility standards, ensured safe deployment. This methodology offers a structured blueprint for adoption in laboratory-scale and small-scale renewable energy facilities, with potential for scaling to utility-scale environments. Overall, the study demonstrates that AI-driven predictive maintenance can significantly improve reliability, operational efficiency, and cost-effectiveness in renewable energy operations.