This study explores credit card fraud detection methodologies with the goal of improving banking safety and reliability. By reviewing existing literature, the study identifies inadequacies in current methodologies, particularly in dataset balancing, feature importance analysis, and model interpretability. This research reviews both automation and conventional approaches to credit card fraud identification by a comprehensive examination of the literature. The paper suggests an advanced procedure that includes gathering data, preprocessing, training the model, analyzing the results, and assessing performance evaluation. It primarily focuses on utilizing techniques from machine learning (ML) and deep learning (DL) to improve the usability and correctness of detection. This research paper presents a comparison of different machine learning and deep learning approaches such as Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN) to detect credit card fraud and the Performance measures like F1-score, recall, accuracy, and precision are measured in order to determine how well certain algorithms work. To further enhance detection abilities, the suggested techniques additionally include innovative methods like feature importance analysis and hybrid dataset selection. To sum up, this work attempts to improve credit card fraud detection by addressing significant shortcomings in existing models. It aims to enhance representation and comprehension of fraud detection factors using feature importance analysis and hybrid dataset selection approach. An effective outcome will be expected through machine learning and deep learning techniques. Building trust is the goal of increasing model interpretability. The overall objective of the research is to increase the reliability of financial transactions by using creative detection techniques.