MLJAR-supervised is an automated machine learning framework that handles the full ML pipeline including feature engineering, algorithm selection, hyperparameter optimization, and model explanation. It supports popular libraries like scikit-learn, XGBoost, LightGBM, CatBoost, and neural networks. The package produces model documentation and explanations automatically, making it suitable for both rapid prototyping and production deployment of tabular ML solutions.