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Desertification risk assessment is important in order to take proper measures for its prevention. Present research intends to identify the areas under risk of desertification along with their severity in terms of degradation in natural parameters. An integrated model with fuzzy membership analysis, fuzzy rule-based inference system and geospatial techniques was adopted, including five specific natural parameters namely slope, soil pH, soil depth, soil texture and NDVI. Individual parameters were classified according to their deviation from mean. Membership of each individual values to be in a certain class was derived using the normal probability density function of that class. Thus if a single class of a single parameter is with mean mu and standard deviation sigma, the values falling beyond mu + 2 sigma and mu -aEuro parts per thousand 2 sigma are not representing that class, but a transitional zone between two subsequent classes. These are the most important areas in terms of degradation, as they have the lowest probability to be in a certain class, hence highest probability to be extended or narrowed down in next or previous class respectively. Eventually, these are the values which can be easily altered, under extrogenic influences, hence are identified as risk areas. The overall desertification risk is derived by incorporating the different risk severity of each parameter using fuzzy rule-based interference system in GIS environment. Multicriteria based geo-statistics are applied to locate the areas under different severity of desertification risk. The study revealed that in Kota, various anthropogenic pressures are accelerating land deterioration, coupled with natural erosive forces. Four major sources of desertification in Kota are, namely Gully and Ravine erosion, inappropriate mining practices, growing urbanization and random deforestation.