Predictive maintenance is essential for enhancing the efficiency of industrial systems. However, the increasing complexity of multiclass datasets, characterized by limited features and small sample sizes, presents significant challenges to developing robust prediction models. This paper investigates various methodologies for predictive maintenance in constrained environments, with a focus on rail systems using simulated fault data. The study evaluates multiple approaches, including ensemble learning models, the TabNet architecture combined with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, and the Retrieval-Augmented Generation (RAG) framework to improve predictive accuracy with sparse datasets. Additionally, the efficacy of cloud-based predictive maintenance models, specifically the AWS SageMaker Linear Learner, is assessed. This research provides a comprehensive evaluation of traditional and contemporary approaches to predictive maintenance when dealing with sparse, high-dimensional and imbalanced datasets.