This paper is about detecting malware using machine learning. As the fact that complex computer attacks are increasing rapidly, suggests that relying on old methods and techniques to identify them are insufficient. So to deal with this situation, machine learning has emerged as the most efficient approach in identification of malware. This paper starts with a brief introduction of different varieties of malware and how the danger is constantly evolving. Then, it explains basic principles and operations of ML models and how it learn from the existing data and mistakes. It discuss about several challenges associated with the use of ML technology in searching for malware. Finally, it talks about what could be done in the future to make it even better at detecting malware with the help of machine learning. This paper will also help other researchers, people who work in cyber security, and anyone else who wants to know more about using ML to look for malware.