E-commerce is experiencing rapid growth with a significant rise in online commerce. As more consumers turn to buying products online to save money and time, it has become common for customers to check product ratings and reviews before purchasing. The impact of reviews on purchasing decisions is evident as positive reviews tend to drive increased purchases, while negative ones can lead to a decline in sales. In those reviews, there is a risk of fraudulent reviews intended to benefit vendors by boosting sales and branding. This deceptive practice can increase counterfeit and abandoned products, often causing harmful effects on buyers. In response to this issue, we devised a spam review detection system using Natural Language Processing and a combination of classification algorithms that analyzes sentiment on the product review dataset by considering the posted date and the user who posted the review. The proposed model will classify human-generated text as genuine and machine-generated text as spam reviews. The case study is based on Amazon India’s E-commerce platform. The negative impact of fraudulent reviews on consumer confidence cannot be overstated, and we must address this issue to maintain the trust of online shoppers.