To improve user experience and influence purchase decisions, e-commerce systems mostly rely on user ratings and tailored suggestions. However, the growing number of fraudulent reviews damages consumer confidence and compromises the accuracy of recommendations. In order to increase the dependability of e-commerce, this research proposes an integrated system that combines a sentiment-driven, feature-based product recommendation model with a false review detection method. GloVe embeddings, KMeans clustering, and sentiment analysis models like CatBoost and LightGBM are used to assess customer reviews. The Isolation Forest method, which is based on anomaly detection, is used to simultaneously identify and filter fraudulent reviews. The system’s overall accuracy significantly improved after using false review detection, illustrating the influence of sincere input on suggestion quality. Results from experiments confirm that removing fraudulent reviews improves model performance while giving consumers more accurate, tailored product recommendations. For contemporary e-commerce platforms, the suggested system provides a scalable way to boost consumer confidence.