Credit card fraud poses a significant challenge in the digital era, necessitating advanced techniques for early detection and prevention. This study presents a comprehensive exploration into the design and implementation of a credit card fraud detection system leveraging machine learning models, specifically Random Forest and Logistic Regression. The research methodology involves preprocessing a diverse and extensive credit card transaction dataset, encompassing various transaction features. Through careful feature engineering, the dataset is prepared for training and testing the Random Forest and Logistic Regression models. The Random Forest model, employing ensemble learning, amalgamates multiple decision trees to enhance predictive accuracy and resilience against over fitting. Concurrently, Logistic Regression, a classical statistical method, analyzes the relationship between input features and the likelihood of fraudulent transactions. The comparative analysis of these models provides insights into their respective strengths and weaknesses, aiding in the selection of the most effective model for credit card fraud detection. The evaluation phase assesses the performance of the models using key metrics such as accuracy, precision, recall, and F1-score. A detailed examination of these metrics under various scenarios sheds light on the models' ability to distinguish between legitimate and fraudulent transactions. Real-world implications of implementing these models in financial institutions or credit card companies are discussed, emphasizing the potential for enhanced security and reduced financial losses. Moreover, this study discusses the ethical considerations and challenges associated with deploying machine learning models in fraud detection systems. Privacy concerns, model interpretability, and the dynamic nature of fraud patterns are acknowledged, providing a holistic view of the practical implications of implementing such systems. Finally, the findings of this research contribute valuable insights to the ongoing efforts in combating credit card fraud. The comprehensive analysis of Random Forest and Logistic Regression models, coupled with real-world applicability and ethical considerations, positions this study as a significant advancement in the field of financial security and fraud prevention.