Flooding presents a significant threat to critical infrastructures (CIs), particularly in Spain, which is frequently cited as the most flood-affected country in Europe. The transportation sector, a crucial CI, is particularly susceptible to such events. It is imperative to monitor the corresponding resilience to improve disaster management efforts. With the advancements in data science and machine learning, various approaches have been developed in this area; however, researchers encounter challenges such as data inconsistency and reliability. This paper presents a resilience assessment framework that utilises machine learning and open data to evaluate the impact of floods on Spain's transportation network. The analysis aims to facilitate well-informed decision-making by stakeholders and government entities, thereby enhancing disaster preparedness and response.