The growing importance of e-commerce in recent years is very significant and its success often depends on customer reviews. Customer reviews have a significant impact on a seller’s profit margin. These reviews or opinions are further classified as sentiments, describing them as a feeling that conveys judgment, attitude, or thought. The core idea presented here is the analysis of customer sentiments through their product reviews, giving recommendations, and a cost comparison of the same product with other e-commerce websites. A proposed solution involves implementing a system utilizing advanced technologies like NLP and AI, employing models such as CNN, BiGRU, CNN-GRU, LSTM, and Bi-LSTM. Among these, BiLSTM emerges as the optimal sentiment analysis model. It emphasizes the critical role of customer reviews in e-commerce, how they can impact a company’s profitability and the challenges associated with managing and analyzing a large volume of reviews. The proposed system extends its functionality to include a product recommendation feature based on sentiment score, review ratings, verified purchase, review length, product popularity, price range additional features. Additionally, the system conducts a cost comparison of the same product across various e-commerce websites. This automation not only enhances the efficiency of extracting valuable insights from customer feedback but also facilitates recommending products based on reviews and identifying sites with comparatively lower prices.