Linear infrastructures such as railways and highways are critical for emergency response in the aftermath of earthquakes, landslides, and other natural hazards. However, they are also highly exposed to structural damage, making timely assessment and restoration essential to ensure resilience and minimize downtime. In recent years, growing attention has been given to monitoring these infrastructures and their surroundings to predict and evaluate damage, particularly structural, resulting from such events. Fixed-wing unmanned aerial vehicles (FW-UAVs) are emerging as a valuable tool in this context due to their rapid deployment, ability to cover large areas, operator safety, and capacity to carry advanced sensors. When pre-event survey data are available, FW-UAVs can support the estimation of structural vulnerability through data-driven models and artificial intelligence, enabling the creation of detailed 3D interactive maps in a short time. These models are also useful for infrastructure maintenance, emergency planning, and response coordination. Despite their potential, the use of FW-UAVs in countries with complex topography and high seismic risk, such as Italy, remains limited. This study aims to synthesize existing literature and explore the potential of FW-UAVs in enhancing infrastructure safety. A practical methodology is proposed, supported by a real-world case study involving transportation infrastructure, to illustrate the significant advantages this technology offers in terms of prevention, damage assessment, and resilience improvement.