There are several websites today that compare products. However, the majority of them only use textual data. Through the creation of Content-based image retrieval (CBIR), the visual software for product photos presented in this work provides a revolutionary technique for visually locating products. Consumers' comparison-purchasing strategy is influenced by product value, complexity, and durability. Comparing prices is frequently used to describe contrast searches as a whole. Contrast searching is expanding beyond just finding the cheapest greenest goods online, though. Nowadays, a vast variety of things are available online. Customers can access various records about the products they are interested in by using production advice structures. The core of this problem from a computer and technology perspective is to extract information from the items so that it can be utilized to match the related products. In order to fit a collection of goods gathered in a database, this work provides a method that merges records from items and, as a result, the outline of the products (textual format). With the help of the comprehension offered by the usage of CNN technique, the definition of the products is integrated. A simple weighting technique is used to integrate photo and text content information. Cosine similarity measurement is used to execute the matching. Online retailers provided the information. The processing of the product reviews has been done using NLP. Android app to compare product prices while using reviews and pricing as help.