Recommendation Systems help users select appropriate products or services from a wide range of choices. Thus, It solves the problem of information overload upto a remarkable extent. Specifically, It is highly applicable in certain industries that sell the product online or provide the services online. Recommendation Systems are very relevant in such a domain because they can grow their business by putting it in the practice. In this review article, we offer an overview of the Recommendation Systems and their variations and extension. We address the numerous techniques used for Recommendation Systems, including content-based filtering, collaborative filtering, sequential, session-based, etc. A comparison has been given for each technique for detailed analysis. It extends the review for the variety of dataset domains, such as movies, music, jobs, products, books, etc. Besides datasets, We have discussed various applications of the recommendation Systems across multiple domains in the industry. We survey various evaluation metrics used in a wide range of Recommendation Systems. In the end, we summarized the different challenges posed by the recommendation Systems, which helps make them more accurate and reliable.