Due to the rising incidence of fraudulent transactions, credit card fraud detection is a critical concern for the banking industry. Traditional systems face a challenge from the fact that the evolving fraud patterns reach new heights; hence, advanced machine learning approaches have become necessary. The present work builds a voting-based machine learning approach and applies four hybrid models: SVM-DF (support vector machine—deep forest), SVM-RF (support vector machine—random forest), DF-RF (deep forest—random forest), and RF-ET (random forest—extra trees). The models are geared toward several domain-specific aspects like merchant, category, amount, user details, demographics, and geographical issues taken from a setting. Using measures to verify performance including accuracy, precision, recall, and F1-score, we show that DF-RF has 95% accuracy, outperforming all other models. The results demonstrate that ensemble learning may serve to enhance the efficacy in detecting fraudulent transactions while keeping the false positives as low as possible. This approach provides a very strong solution for financial institutions, which could certainly improve fraud detection strategies while cutting risks for money losses.