One of the key processes of MCDA is its ability to combine the values of multiple criteria with a single evaluation score for each alternative decision [9]. Thus, the integration of MDCA methods in the spatial domain provides a proper framework for fire risk assessment [10]. In order to identify the regions susceptible to fire and determine forest fire risk, various studies have been carried out worldwide. Among many methods used for modelling of forest fire susceptibility are index-based method [11; 12], fuzzy analytic hierarchy process (AHP) [9; 10; 13; 14], and fuzzy analytic network process (ANP) [15]. Since conventional AHP technique might not be adequate to provide effective decision in forest fire susceptibility mapping, fuzzy AHP technique has been used to address this issue by enabling decision makers to define approximate preferences through fuzzy numbers. This gives a much better and more precise representation of the relationship between criteria and alternatives [16]. The most significant contribution of this research is that the fuzzy AHP method was used for the first time for forest fire susceptibility assessment in Serbia. The fact that it was used on the territory of one of the most important protected natural areas reinforces that significance. Additionally, it compares results of index-based methods and fuzzy AHP using the technique for order of preference by similarity to ideal solution (TOPSIS) method. No less important is the fact that the data and software (QGIS, RStudio) used for forest fire susceptibility zonation is completely open source. Zero cost provided in such a way should help better forest fire risk management for communities that have a low budget when it comes to nature protection. Although it is well known that historical data of forest fires that occurred in certain areas and relevant meteorological data are important for comprehensive forest fire risk assessment, this paper shows that it is possible to perform suitable forest fire susceptibility zonation even if such datasets are not available. Another contribution of this paper is the improvement of classification of land use/land cover/vegetation based on the remote sensing data and the improvement of the random forest (RF) method in such a classification. As stated, in this study, we deployed the fuzzy AHP method without historical data of forest fires that occurred in a given area (Nature Park Golija) and relevant meteorological data used as inputs. In some previous studies [14; 15; 17; 18; 19; 20], meteorological data, marked also as climate or weather data, were used as inputs to fuzzy AHP methods, along with other parameters (i.e., topographic, vegetation, human), in order to assess forest fire susceptibility. It can be argued that by using local meteorological data, i.e., data collected by the sensors densely deployed in area of interest, we could further improve quality of the forest fire susceptibility assessment in a long-term manner (i.e., monthly or yearly by using averaged data) or in a short-term manner (i.e., daily by using current data). Such an approach would also allow us to gather and use more precise information regarding the influence of global climate changes on local climate parameters (for the given area), which cannot be ignored in the current situation. In the case of protected natural areas (such as NP Golija), this improved insight could be beneficial not only for forest fire prevention, but also for wildlife preservation. Additionally, the inherent real-time nature of the sensor network operation enables us to observe other possible applications in forest fire prevention, detection, and monitoring such as real-time forest fire risk assessment and detection. Thus, as an addition, we here present a general framework for Internet of Thing (IoT)-based sensor networks that could be established in the given area of Nature Park Golija in order to enable continuous collection of local meteorological data.