Hurricanes have caused severe damage to the electric power system throughout the Gulf coast region of the U.S., and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing hurricane risks and properly preparing for post-storm recovery efforts requires rigorous methods for estimating the number and location of power outages, customers without power, and damage to power distribution systems. This dissertation presents a statistical power outage prediction model, a statistical model for predicting the number of customers without power, statistical damage estimation models, and a physical damage estimation model for the gulf coast region of the U.S. The statistical models use negative binomial generalized additive regression models as well as negative binomial generalized linear regression models for estimating the number of power outages, customers without power, damaged poles and damaged transformers in each area of a utility company’s service area. The statistical models developed based on transformed data replace hurricane indicator variables, dummy variables, with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The physical damage estimation model provides reliable predictions of the number of damaged poles for future hurricanes by integrating fragility curves based on structural reliability analysis with observed data through a Bayesian approach. The models were developed using data about power outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast region.