Fire stations play a central role in protection and response activities as part of emergency management services in cases of fire incidences. With the rising urban populations and city expansions, the demand for more fire services resultantly increases. It then becomes critical to effectively plan the location of emergency facilities to adequately service the population and ensure the protection of lives and infrastructure. This study, therefore proposes the use of the fuzzy extension of the Multi-Criteria Decision-Making (MCDM) method of Analytical Hierarchical Process (AHP), hence called fuzzy AHP, integrated with Geographic Information Systems (GIS) approach to optimally site new fire stations for the case of Istanbul region. This proposed fuzzy approach simulates the subjective expert judgements for the preferences of the six criteria assessed for fire station suitability mapping and thereby accounted for the uncertainty of crisp comparison values via triangular fuzzy numbers (TFNs). The criteria weights evaluated from this procedure were used in a weighted overlay analysis of the reclassified criteria map layers in ArcGIS to generate a fire station suitability map. These resultant fuzzy AHP criteria weights were validated using another MCDM technique, called Best-Worst Method (BWM) and found to be comparable and consistent. The criteria that had the strongest influence on the selection of sites for fire stations were identified to be: the density of hazardous material facilities (DHM), a high population density (HPD) and proximity to main roads (PMR) with associated weights of 33.3%, 24.4% and 15.2%, respectively. Based on a thorough assessment within the areas represented by class values ranging from 3 to 5 on the suitability map, a total of 34 new fire station sites were selected complementing the existing 121 fire stations. Further, a prioritization analysis from low, medium to high, was performed to plan the phases for the construction of new fire stations in view of competing budgetary needs and resource constraints. The methodology to achieve this was proposed and modelled for enhancing the decision-making process in urban fire station site selection studies.