Mapping where floods have occurred is essential for emergency response and planning, especially in places where data is limited. Traditional models that simulate floods can be accurate, but they take a lot of time, computer power, and setup. On the other hand, newer machine learning methods are faster but often hard to use because they require complicated data processing. In this project, we created an easy-to-use flood mapping tool on Google Earth Engine (GEE), a cloud-based platform. Our tool uses satellite images and a type of machine learning called Random Forest to map flooded areas without the need for heavy data processing or powerful local computers. We tested our tool in the Galveston Bay area of Texas during Hurricane Harvey and found that it performed well compared to a detailed flood model. Our results show that this new tool can help quickly and accurately map floods after they happen, making it useful for emergency responders and long-term planning.