In today's age of web 2.0, large numbers of product reviews posted on the Internet. Such reviews are important to customers or users and to companies. Customers use the reviews for deciding quality of product to buy. Companies or vendors use opinions to take a decision to improve their sales according to intelligent things done by other competitors. However, all reviews are given by customers or users are not true reviews. These reviews are given to promote or to demote the product. Some reviews are given on brand of product, and others are related to advertising of another product. There is need to find how many reviews are spam or non spam. In this paper, the system is proposed for detecting untruthful spam reviews using n-gram language model and reviews on brand spam detection using Feature Selection. Given system separately identifies spam and joined the result showing spam and non spam reviews.