Hurricanes are a destructive, inevitable force of nature. They have demolished entire cities and have left long-lasting effects on the world. In this paper we take statistical approach to hurricanes using Functional Data Analysis (FDA). Our data for this project consists of 10 Gulf of Mexico hurricanes obtained from the National Hurricane Center of the National Oceanic and Atmospheric Administration (NOAA). We analyze the wind speed of the 10 hurricanes using traditional FDA methodologies such as, functional B-splines, 1st and 2nd derivative, residual analysis from a co-variate model, and outlier detection using functional box-plots. We conclude that Gulf hurricane's wind speed, when modeled using functional data analysis methods, have similar behavior during the beginning and end of their life-cycle that allow for a identification of outlier hurricanes. Furthermore, we discover that the wind speed during the middle of the observed hurricanes' life-cycle is inconsistent and volatile, exacerbated by the length of a hurricane's life. Our novel FDA approach to hurricane analysis can serve as a useful insight to understanding the nature of Gulf Hurricanes wind speed.