Natural disasters like hurricanes can severely disrupt transportation systems, leading to roadway closures and limiting accessibility, which has extreme economic, social, and sustainability implications. This study investigates the impact of hurricanes Ian and Idalia on roadway accessibility in Florida using machine learning techniques. High-resolution satellite imagery, combined with demographic and hurricane-related roadway data, was used to assess the extent of road closures in southeast Florida (Hurricane Ian) and northwest Florida (Hurricane Idalia). The model detected roadway segments as open, partially closed, or fully closed, achieving an overall accuracy of 89%, with confidence levels of 92% and 85% for the two hurricanes, respectively. The results showed that heavily populated coastal regions experienced the most significant disruptions, with more extensive closures and reduced accessibility. This research demonstrates how machine learning can enhance disaster recovery efforts by identifying critical infrastructure in need of immediate attention, supporting sustainable resilience in post-hurricane recovery. The findings suggest that integrating such methods into disaster planning can improve the efficiency and sustainability of recovery operations, helping to allocate resources more effectively in future disaster events.