On the Internet, where the number of choices is overwhelming, there is a need to filter, prioritize and efficiently deliver relevant information to alleviate the problem of information overload, which has created a potential problem for many Internet users. Alternate Medicine System solves this problem by searching through a large volume of medical information to provide users with filters and services. This project explores the different characteristics and potentials of different recommendation techniques in recommendation systems to serve as a compass for research and practice in the field of medical recommendation systems. In this project, we have used a dataset over 100+ medicines from different companies and brands to do recommendation based on the content of medicine and then filter it based on rating and cost-based analysis. Through experimental results, we have found that more than 95% of the medicines have a lower cost based alternative available with a higher rating.