The proliferation of fake product reviews in the E-commerce industry has emerged as a significant challenge, undermining consumer trust and integrity in online platforms. This project addresses this pressing issue by developing an advanced system for detecting and eliminating fake reviews using cutting-edge methods for machine learning (ML) and natural language processing (NLP). The system integrates models such as Random Forest, Naive Bayes, MLP, and a Voting Classifier, trained on the “Amazon Yelp dataset” to ensure scalability and adaptability for large-scale applications. By providing a real-time assessment of review authenticity, the system empowers platform owners to take informed actions against spurious content, thereby preserving the integrity of online shopping experiences. The project highlights the importance of employing advanced NLP and ML techniques in combating fake reviews and contributes significantly to enhancing user trust in the online marketplace. The project aims to address the critical need for effective fake review detection and elimination systems in the E-commerce industry. The escalating prevalence of fake reviews on prominent platforms like Flipkart and Amazon undermines consumer trust, highlighting the urgency for a robust solution. By leveraging the “Amazon Yelp dataset” for model training, the study emphasizes scalability and adaptability for large-scale applications. Recognizing the escalating impact of fake reviews on user trust, this research addresses the critical need for platforms to combat spammers and keep alive the integrity of online shopping experiences. The proposed model not only provides a real-time assessment of review authenticity but also offers a foundation for website owners to take informed actions against spurious content. With potential applications for platforms of varying sizes, this sophisticated model demonstrates its efficacy in detecting spam reviews, contributing to the enhancement of user trust in the online marketplace.