Mapping the limits of areas that are flood-prone due to possible water control infrastructure failure is a critical issue. Practical difficulties are encountered that often pose major challenges to the accurate and effective analysis of dam-break hazard areas. These studies are expensive, lengthy and require large amounts of incoming data and refined technical skills. Creating cost-efficient geospatial tools makes it possible to obtain rapid and inexpensive estimates of areas that can be flooded following instantaneous dam-break (caused by structural failure); however, these estimates should be considered to supplement the results of hydrodynamic simulations, which they cannot replace. This study implements a Geographic Information System (GIS) based method that can provide useful information on the identification of dam-break flood-prone areas in both data-scarce environments and transboundary regions, in the absence of detailed studies. Moreover, the proposed tool makes it possible to analyse a wide number of case studies that support the prioritization of interventions, or, in emergency situations, the simulation of numerous initial assumptions (e.g., the modification of initial water level/volume in the case of limited dam functionality), and this with no need for advanced technical skills or time-consuming calculations The proposed model is based on the commonly available data for masonry dams, i.e., dam geometry (e.g., reservoir capacity, dam height and crest length), and a Digital Elevation Model. The model allows for rapid and cost-effective dam-break hazard mapping by evaluating three components: (i) the dam-failure discharge hydrograph, (ii) the propagation of the flood, and (iii) the delineation of flood-prone areas. The tool showed high accuracy and reliability in identifying potential dam-break flood-prone areas when compared to the results of traditional hydrodynamic approaches, as applied to a dam in Basilicata (Southern Italy). In particular, for the San Giuliano dam (Basilicata), the over- and under-estimation rates of the proposed tool were assessed by comparing its outputs with flood inundation maps that were obtained by two traditional methods, using a one-dimensional and a two-dimensional propagation model, resulting in a specificity of roughly 90%. These results confirm that the proposed GIS model correctly classified most parts of the flood map as flood-prone. A sensitivity of over 75% confirms that several areas were also correctly identified as non-flood-prone. In addition, the overall effectiveness and reliability of the proposed model were evaluated with reference to the Gleno Dam (Central Italian Alps), by comparing the results of literature studies on the application of monodimensional numerical models and the extent of the flooded area as reconstructed based on the available historical information, thus obtaining an accuracy of around 94%. Finally, the computational efficiency of the proposed tool was tested though demonstrative application to 250 Italian arch and gravity dams. A PC with Pentium Intel Core i5 Processor CPU 3.2 Ghz and 8 GB RAM was used, which took about 73 minutes to produce the results, which showed the potential of applying this tool to dam-break flood mapping for a large number of dams.