The advent of smart electric drivetrains in modern transportation systems, particularly in the railway and automotive sectors, has led to a critical demand for robust health monitoring solutions. Ensuring operational reliability, minimizing downtime, and extending the service life of drivetrain components such as electric motors, inverters, gearboxes, and batteries are pivotal for system efficiency and safety. Artificial Intelligence (AI) has emerged as a transformative approach in predictive maintenance by enabling early fault detection, remaining useful life (RUL) prediction, and condition classification through intelligent data analysis. This paper presents a comprehensive comparative study of various AI algorithms deployed for health monitoring of smart electric drivetrain components. Both traditional machine learning techniques—such as Support Vector Machines (SVM), Random Forests, and k-Nearest Neighbors (k-NN)—and modern deep learning architectures—such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and hybrid models—are critically reviewed. The performance of these algorithms is assessed based on key evaluation parameters including accuracy, computational complexity, real-time applicability, data dependency, and adaptability to non-stationary conditions. By synthesizing findings from diverse application domains, this study highlights the strengths and limitations of each algorithm in practical deployments. Furthermore, open challenges, such as dataset scarcity, sensor noise, and model interpretability, are discussed, along with potential directions for future research. The insights provided aim to guide researchers and engineers in selecting appropriate AI strategies for effective drivetrain health management.

Keywords: Artificial Intelligence, Smart Electric Drivetrain, Health Monitoring, Predictive Maintenance, Machine Learning, Fault Detection