Nowadays, significant macroeconomic challenges confront nations worldwide, jeopardizing their capacity to endure and prosper. Banks play a sensitive role in addressing such a dilemma by reallocating financial resources to the most qualified and productive ones to help stabilize the economies. Additionally, emerging economies are more sensitive to such challenges than developed ones and need more attention and comprehension of the factors that affect their economic and financial positions. Moreover, the paper found limited studies applied in the Middle East to predict bank performance using machine learning techniques. In this regard, the paper aims to investigate the determinants of bank performance using machine learning algorithms taking a sample from the banks of Egypt using the Random Forest and Logistic Regression models to predict the financial performance of the banks. The findings showed that corporate credit risk and capital adequacy ratio are the most significant variables and can provide a solid foundation for forecasting bank performance. Additionally, the two models in use have a high degree of accuracy—94%—in predicting bank performance. This will assist professionals in using the models as a warning system for potential problems and to enhance their prediction performance to develop stronger financial positions that support and foster economic growth.