In this technological era, social media has become one of main sources of information, people go to social media to get or even post real-time information about any on-going situation, the same goes for disaster events. This widespread usage of social media platforms is considered to be highly beneficial for emergency teams and decision makers as time is a very critical aspect during disasters and relying on traditional sources of information, like television stations, could take hours. However, information cannot be taken from social media posts directly, loads of posts are posted on social media during disasters, and a large percentage of these posts include spams, replicated posts and other posts that are not informative. Therefore, the aim of our project is to use Deep Learning (DL) and Machine Learning (ML) techniques to identify infor- mative disaster-related posts on social media streams.