According to the Center for Research on the Epidemiology of Disasters (CRED), floods are the most frequent and devastating natural disaster globally, significantly impacting populations and economies. Floods already affect more than 1.81 billion people. Future projections indicate an increase in exposure to this phenomenon due to population growth and climate change, making it essential for the scientific community to understand the factors influencing flood risk. Flood risk is determined by three main components: hazard, referring to the processes that lead to high flood levels; exposure, indicating the assets and people at risk; and vulnerability, describing how susceptible these elements are to damage. Only a thorough analysis of these aspects can enable the formulation of effective mitigation strategies. This thesis aims to contribute to the understanding of large-scale flood risk, focusing specifically on Italy, through the analysis of probabilistic risk models applied over time. To this scope, we introduce the RESCUE (laRgE SCale inUndation modEl) framework, an innovative approach that combines geomorphological analysis with hydrological and hydraulic modeling, allowing for accurate large-scale flood mapping. The RESCUE flood hazard model is combined with a damage model accounting for economic direct damages and population affected, in the RESCUE-FR (Flood Risk) framework, exploring how uncertainties in hydrological and hydraulic models affect risk assessments. The framework is tested on case studies focusing on Central Italy, the importance of considering uncertainty to provide reliable estimates of damage and affected populations is illustrated and discussed. The evolution of flood risk in Italy over a period of 230 years, highlighting the changing drivers of risk over time. Projections under high emission scenarios suggest that the impacts of climate change will surpass those of population growth, indicating the need for proactive approaches to risk management.