In today’s world, fake review identification and prediction is an importantarea of sentiment analysis of the E-commerce industry. The automatic fake review categorizersidentify and categorize a variety of duplicate, spam, fake and untrustworthy reviews using machine learning techniques. This paper studies various recent existing fake review detection methods using NB and RF classifiers for the Yelp and Flipkart datasets. It provides a detailed study on various fake review predictors and compares their basic and performance-based specifications. It highlights the challenges, threats, and gaps of these existing works. Further, it graphically shows the discrimination for the specifications of year-wise evolution, classifier usage, and dataset usage.