Nowadays, with machine learning having a significant impact, detecting fraud in online transactions has become crucial. In today’s world of rapid change, machine learning algorithms are helping us keep ahead of fraud by identifying trends and anomalies that traditional methods overlook. These models are incredibly useful tools for combating fraud since they continuously learn from fresh data and get better over time. We’ll examine how several machine learning models are applied to identify different kinds of fraud in this post. We’ll also discuss the several issues that arise when utilizing these technologies, such as managing processing costs, selecting the appropriate features, and guaranteeing data quality. By weighing the advantages and disadvantages of the techniques discussed, this paper offers a useful look at how they are applied in actual fraud detection. This study examines various machine learning techniques, lays out future directions for developing more effective and comprehensive fraud detection solutions, and links academic knowledge with practical industry needs to provide a clear picture of the present and future role of machine learning.