Predicting credit scores accurately is crucial for financial institutions to assess credit risk and make informed lending decisions. This study explores the application of machine learning algorithms to forecast credit scores using a dataset of 100,001 entries. Key features such as credit history age, annual income, interest rate, and number of credit cards were selected for analysis, while the credit score served as the target variable. The study evaluates the predictive performance of machine learning models like logistic regression, K-Nearest Neighbours, Support Vector Machine and Random Forest, focusing on their ability to identify patterns and relationships between the selected features and credit scores. Insights into feature importance and model interpretability are also discussed, emphasizing the value of data-driven approaches in improving credit risk assessment. The results demonstrate the potential of machine learning to enhance the precision and efficiency of credit scoring, benefiting financial institutions and customers alike.