Fraud detection in financial services is a vital function that demands real-time analysis to minimize losses and safeguard customer accounts. This research investigates how cloud-based machine learning (ML) can implement a real-time fraud detection system. We developed a scalable and responsive fraud detection pipeline by integrating cloud infrastructure with advanced ml algorithms. This architecture leverages cloud resources for high-throughput processing and efficient model training, enabling it to adapt smoothly to changing transaction volumes. Our approach encompasses feature engineering, real-time data streaming, model deployment, and performance evaluation within a cloud environment, achieving both speed and accuracy in identifying fraudulent activities. As organizations increasingly aim to improve strategic decision-making, cloud-based solutions offer scalable, efficient, and cost-effective data processing and analytics platforms. This framework showcases a cloud-enabled ML solution for real-time fraud detection in financial services, demonstrating how sophisticated ML techniques can extract valuable insights from large transaction datasets, enabling an adaptive pipeline capable of handling dynamic transaction demands.