Inundation has been occurred in Japanese urban areas. Simulations are often used to assess pluvial flood risk, but such approaches tend to be highly complicated and cannot evaluate detailed topography. Therefore, this study aims to analyze the characteristics of flooded areas and make a potential map for pluvial flooding using high resolution data and machine learning. We extracted some indices related to roads and topography, and they were analyzed using Random Forest. The study areas in Tokyo were classified into flooded and non-flooded area with accuracies about 80-90%.