Financial distress prediction has become a crucial area of research around the world to reduce economic instability, and machine learning holds tremendous potential to improve financial decision-making in the dynamic market context of Vietnam. In this paper, we classify data into levels of financial distress using the Altman Z-score mothed and uses ANN, XGBoost, Random Forest Classification, SVM, and Gradient Boosting Machine models to test the predictive accuracy of financial distress in manufacturing, service, and trade by using financial ratio data of over 700 Vietnamese companies during 2009–2019. Techniques such as SMOTE to handle class imbalance, Principal Component Analysis (PCA) for dimensionality reduction, and tuning of hyperparameters were used to further optimize model performance. The proposed models showed an overall accuracy of 95.763% in classifying the listed corporations as low, high, and intermediate financial distress levels. For manufacturing firms, the accuracy was 94.531%, followed by the trade sector at 93.217%, while that for the service sector was also very strong, with a predictive capability of 91.382%. The results revealed that SVM and ANN outperformed other models. Notably, hyperparameter tuning significantly enhanced the models’ predictive performance across all sectors. A web application is developed based on Streamlit to show model visualizations and interaction.