This paper proposes effective and scalable approaches for detecting credit card fraud in order to handle the increasing complexity and volume of financial transactions within digital ecosystems. The primary goal of the investigation is to identify fraudulent activity in real time by employing cutting-edge machine learning techniques, including ensemble models, anomaly detection, and deep learning algorithms. The system is engineered to efficiently manage large-scale transactional information by prioritizing scalability through the implementation of superior data management techniques and distributed processing frameworks. The proposed system also incorporates adaptive learning techniques to mitigate false positives and adapt to changing fraud patterns. The framework is an appealing choice for modern financial institutions in search of robust fraud prevention systems due to its high detection accuracy, enhanced processing efficiency, and long-lasting performance.