Bankruptcy is a lawful procedure when an organization is not able to repay its financial obligations to its investors. In this situation, one may lose their valuable possessions and disrupt regular industrial activities while paying off debts. This aforementioned situation has a significant impact on the financial stability of both individuals and organizations across the globe. Several works happened where different machine learning models were used to predict bankruptcy situations of different industries. In this work, we propose an Automated Machine Learning (AutoML)-based model that predicts the bankruptcy of different organizations more efficiently. First, the primary bankruptcy dataset was gathered from a public repository and preprocessed it for further processing. This dataset was balanced by oversampling with the Synthetic Minority Oversampling TEchnique (SMOTE) and undersampling with Random UnderSampling (RUS) techniques. To conduct the AutoML process, we employed the Tree-based Pipeline Optimization Tool (TPOT) framework in the primary, balanced oversampling, and undersampling datasets. After evaluating the classification outcomes of the primary and its balanced dataset, the best pipeline of Gradient Boosting in TPOTClassifier showed the best accuracy of 99.28% for the SMOTE oversampled dataset to predict bankruptcy more appropriately. This method also reduces many limitations of existing models and can be also used as a complementary tool in different machine learning applications.