The revolutionary incorporation of machine learning (ML) into Big Data performance assessment techniques is examined in this article. Making sure the system performs at its best becomes crucial when companies struggle to manage large datasets. Large-scale distributed systems provide challenges for traditional testing procedures, which is why adaptive methodologies are becoming more popular. The study explores the relationship between big data, ML, and performance testing, highlighting important issues and accepted practices. With its capabilities for anomaly detection, automated test case creation, predictive analytics, and dynamic resource allocation, ML emerges as a transformative tool. This paper compares various ML techniques such as regression model, decision tree, random forest, gradient boosting, neural networks, support vector machine, and clustering algorithms, on the basis of accuracy, response time, and latency. This thorough analysis provides researchers, practitioners, and decision-makers with new perspectives on how to use ML to transform Big Data performance assessment.