This research paper presents an innovative method for identifying credit card fraud by employing a stacking ensemble model. This approach combines several machine learning techniques to enhance detection precision and minimize false-positive results. Credit card fraud detection is complex due to data imbalance, with legitimate transactions vastly outnumbering fraudulent ones. This study utilizes a publicly available credit card transactions dataset from European cardholders, containing 284,807 records with 492 fraudulent cases (0.172%). The dataset features numerical attributes derived from PCA, alongside “Time” and “Amount” offering a robust basis for analyzing fraud detection in highly imbalanced scenarios. This work addresses the imbalance challenge by designing a robust ensemble model without the use of over-sampling techniques. The stacking ensemble integrates Logistic Regression, Random Forest, and XGBoost models, each contributing unique strengths: Logistic Regression enhances detection of linear relationships, while Random Forest and XGBoost capture intricate non-linear patterns, enhancing predictive accuracy. The combined model setup demonstrates exceptional performance indicators, boasting an accuracy rate of 99.95% precision of 97, recall of 81, and an F1 score of 85. These outcomes underscore the efficacy of collaborative learning approaches in enhancing fraud detection systems, presenting a promising framework for minimizing financial risks in practical applications. The findings demonstrate the model’s capacity to adapt to evolving fraud patterns, offering scalable solutions for integration into real-world financial infrastructures to bolster detection accuracy and minimize operational risks. These findings highlight the broader implications of the proposed model in improving fraud detection systems by enhancing accuracy and scalability for real-world financial applications.