Significant obstacles to financial security have arisen as a result of the quick uptake of Unified Payments Interface (UPI) for online transactions and a commensurate rise in fraudulent activity. This paper suggests a novel fraud detection method that makes use of cutting-edge machine learning (ML) algorithms to address this urgent issue. It focuses on integrating a Hidden Markov Model (HMM) into the UPI transaction process. In order to enable the system to identify departures from these learnt behaviors as possibly fraudulent, the HMM is trained to predict the typical transaction patterns for particular cardholders. The suggested system uses a variety of contemporary approaches, such as Kmeans Clustering, Auto Encoder, Local Outlier Factor, and artificial neural networks, to improve algorithmic diversity and flexibility to changing fraud patterns. In addition to addressing issues like test data creation for training and validation, the system emphasizes a heuristic approach to solving high-complexity computational problems, guaranteeing efficacy in a variety of settings. This study, which is positioned as a proactive and adaptable solution, emphasizes how crucial it is to stop UPI fraud and provides a thorough foundation for reliable fraud detection in the ever-changing world of online transactions.