This study aimed to analyze the application of machine learning techniques from artificial intelligence to improve credit risk assessment in Syrian banks. The study sample included historical financial data and demographic information of bank customers. Machine learning methods were used to analyze the data and develop a model for accurately predicting credit risks. Techniques such as random forests and neural networks were utilized for data analysis and model building. The study found that the use of machine learning techniques, such as random forests and neural networks, can significantly enhance the accuracy of credit risk assessments compared to traditional methods. This improvement helps reduce default rates and increase bank profitability, enhancing financial stability and economic growth. Additionally, the results showed the superiority of machine learning-based models in early detection of potential risks, enabling banks to take effective preventive measures. The results indicated that applying these techniques contributes to improving banking operations' efficiency and reducing costs, allowing banks to offer better services to customers and enhance their competitive ability in the market. The study also highlighted the challenges associated with integrating these technologies with current systems, including the need for continuous employee training and updating technological infrastructure to ensure optimal use of machine learning techniques. In light of the study results, the researcher presented several recommendations including promoting the use of machine learning techniques in Syrian banks, providing technical support and continuous training for employees, and leveraging successful global experiences to develop effective strategies for credit risk assessment. The study also recommended establishing specialized units in banks to manage and develop machine learning and artificial intelligence applications. Keywords: Machine Learning, Artificial Intelligence, Banking Risk, Credit Prediction, Syrian Banks