In the early 1980s Geographical Information Sys-tems (GIS) software emerged as a new information processing technology offering unique capabilities of automating, managing, and analysing a variety of spatial data. Many applications of GIS developed over the last decade provided information necessary for the decision-making in diverse areas including natural resource management, regional planning, and disaster management. Two perspectives on developing better decision sup-port capabilities of GIS can be identified, one based on analytical problem solving as a centrepiece of Spatial Decision Support Systems (SDSS) and an-other based on integration of GIS and specialized analytical models. According to first perspective, SDSS should offer modelling, optimization, and sim-ulation functions required to generate, evaluate, recommend, and test the sensitivity or problem solu-tion strategies. These capabilities are essential to solving semi-structured spatial decision-making problems. The second perspective on improving the decision support capabilities focuses on the expan-sion of GIS descriptive, prescriptive, and predictive capabilities by integrating GIS software with other statistical software and analytical models. Accord-ing to this view, mapping, query, and spatial model-ling functions of GIS can provide data display at different scales, preprocessing, and data input for environmental and statistical models. The general objective of Multi-criteria Decision Making (MCDM) is to assist the decision-maker (DM) in selecting the “best” alternative from the number of feasible choice-alternatives under the presence of multiple choice criteria and diverse cri-terion priorities. The problem of multicriterion choice in decision making is the paramount chal-lenge faced by individiuals, public and private cor-porations. The nature of challenge is two-fold: How to identify choice alternatives satisfying the objec-tives of parties involved in the decision-making pro-cess? How to order the set of feasible choice alter-natives to identify the most preferred alternative? The challenge of multicriterion choice can be at-tributed to many spatial decision-making problems involving search and location/allocation of re-sources. These problems, often analysed in (GIS), include location/site selection for: service facilities, retail outlets, critical areas for specific resource management, and emergency service locations where are key locations for effective emergency management. In this study, the criteria and its priorities/weights that should be considered for finding optimal loca-tions of fire stations are determined; and multi-criteria site analysis is conducted based on men-tioned criteria weights in (GIS) environment. More-over, in order to test the sensitivity and robustness of the model developed, a sensitivity analysis is per-formed based on the combination of the criterion weights by using (GIS) capabilities. With these anal-yses performed, it is focused on the creating the model that supports decision makers in decision-making for finding the optimal locations of fire sta-tions. In this study, these steps are followed: Definition of the problem/objective (determining the optimal locations of fire stations); determining the potential criteria in finding the optimal locations of fire stations; data collection and preparation and transfer to (GIS) environment; creation of raster data sets representing the regionalised criteria; classification of raster data sets; establishment of preference matrix, assigning preference values to the relevant criteria by using the pairwise compari-son feature of Analytic Hiyerarchy Process (AHP); determination of criteria weights by calculating ei-genvalues and eigenvectors of the preference matrix which evaluated by two decision maker group; de-termining the criteria priorities/weights values by using the synthesis of priorities and calculating the overall composite weights; calculating the result raster (suitability map for potential fire stations) as a weighted summation of all criteria raster data sets; conducting the sensitivity analyses in (GIS) en-vironment in order to test the sensitiveness and ro-bustness of the model developed; offering a system that supports decision makers in determining the optimal locations of fire stations. The integration of the (AHP) and (GIS) combines decision support methodology with powerful visuali-sation and analysing capabilities which should con-siderably facilitate finding optimal locations of fire stations and this process improves the decision mak-ing in emergency management.