Recently it is obvious that climate change has became a critical topic for human society. As climate change becomes more severe, natural disasters caused by climate change have increasingly impacted humans. Most recently, Hurricane Ida killed 43 people across four states. Hurricane Ida's damage could top $95 billion, and many meteorologists predict that climate change is making storms wetter and wider. Thus, there is an urgent need to predict how much damage the flood will cause and prepare for possible destruction. Most current flood damage estimation system did not apply social media data. The theme of this thesis was to evaluate the feasibility of using machine learning models to predict hurricane damage and the input data are social media and satellite imagery. This work involves developing Data Mining approach and a couple of different Machine Learning models that further extract the feature from the data. Satellite imagery is used to identify changes in building structures as well as landscapes, and Twitter data is used to identify damaged locations and the severity of the damage. The features of Twitter posts and satellite imagery were extracted through pre-trained GloVe, ResNet, and VGG models separately. The embedding features were then fed to MLP models for damage level estimation. The models were trained and evaluated on the data. Finally, a case study was performed on the test dataset for hints on improving the models.