Predictive Maintenance (PdM) through Machine Learning (ML) has become an essential strategy for industries to reduce redundant maintenance activities. Even so, many companies lack the technical knowledge to implement these PdM systems, and more often than not, do not trust ML models for their lack of transparency and interpretability. To mitigate these issues, the present paper explores and implements an Automated Machine Learning (AutoML) and Explainable Artificial Intelligence (XAI) framework designated as MLJAR. The framework is evaluated for its AutoML and XAI capabilities in a widely used synthetic PdM dataset in the literature. Promising results were found as the framework was able to outperform most works in the literature by up to 26.6% in recall score, with the only work surpassing MLJAR by up to 3.2%, yet having the drawbacks of 58.3% worse precision score, and no AutoML or XAI capabilities. Overall, the MLJAR framework was able to, on average, provide 13.2% and 2.2% better scores in recall and accuracy, respectively.