Credit card fraud is an eminent problem that has a direct impact on financial organizations; it results in massive losses and loss of customers’ trust. This is because; traditional methods of fraud detection have proven to be ineffective because of the advanced fraud techniques in the market. This paper proposes a new approach to early fraud detection using deep learning and machine learning algorithms with low false alarm rates. In particular, a Convolutional Neural Network (CNN) is combined with machine learning classifiers to enhance the detection performance. To overcome the problem of data irregularities, random sampling is used, while feature engineering and ensemble decision-making optimize the model. The methodology is executed in two stages: first, the dataset is divided into training and testing datasets and then for further refinement the cross-validation is performed. CNN is used for feature extraction to capture the features of a transaction and the algorithms include Extreme Gradient Boosting, Random Forest, K-Nearest Neighbors (K-NN). The model’s accuracy is measured using several statistical measures, which all turn out to be very good. When using cross-validation, the model had an accuracy of 0.9986, precision of 0.9971, the recall score was 1, the F1 score of 0.9986, and the ROC AUC was 0.9986. Thus, the present work reveals the possibilities of improving credit card fraud detection and increasing the general financial security by integrating deep learning and machine learning techniques.