Artificial Intelligence (AI) is transforming professional practices by providing advanced tools for predictive maintenance (PdM), a critical aspect in industries such as wind energy, transportation, and manufacturing. PdM enables timely fault detection, reduces equipment downtime, enhances safety, and decreases the carbon footprint. This study conducts a comparative analysis of AI-driven PdM models, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Emphasis is placed on model interpretability, robustness, and practical applicability in real-world settings. The research highlights how AI integration into PdM improves operational reliability and safety standards while supporting sustainability and humanitarian values. The findings provide actionable recommendations for selecting the most suitable PdM models based on industry-specific requirements and deployment objectives.
artificial intelligence; predictive maintenance; fault detection; sustainability; interpretability; reliability; safety; neural networks; random forest; gradient boosting.