Credit card fraud remains a pervasive and evolving threat in the digital age, necessitating the development of sophisticated methods for early detection and prevention. This study provides a thorough examination of a machine learning-based credit card fraud detection system, employing two prominent algorithms: Random Forest and Logistic Regression. The research methodology involves preprocessing a diverse and extensive credit card transaction dataset, incorporating various transactional features. Through rigorous feature engineering, the dataset is meticulously prepared for model training and validation. The Random Forest model, an ensemble learning technique, aggregates multiple decision trees to improve predictive accuracy and mitigate the risk of over-fitting. In parallel, Logistic Regression—a classical statistical approach—models the probabilistic relationship between transaction features and the likelihood of fraud. A comparative analysis of these models offers valuable insights into their respective strengths and limitations, guiding the selection of the most suitable model for fraud detection. Model performance is evaluated using critical metrics, including accuracy, precision, recall, and F1-score, with a detailed examination of these indicators across different scenarios to assess each model's ability to distinguish between legitimate and fraudulent transactions. Furthermore, the study explores the practical implications of implementing these models in financial institutions, highlighting their potential to enhance security and reduce financial losses. Ethical considerations, including privacy concerns, model interpretability, and the adaptive nature of fraud patterns, are also discussed, providing a comprehensive perspective on the deployment of machine learning in fraud detection systems. Ultimately, this research contributes to the advancement of financial security, offering a robust analysis of Random Forest and Logistic Regression models and their real-world applications in combating credit card fraud.