Foot-and-Mouth (FMD) is a viral and contagious disease that endangers both domestic and wild animals and this disease has caused a lot of social and economic damage to the livestock industry in Iran. Considering the endemism of this disease in Iran, the main purpose of this study is to investigate the temporal trend, discover spatial and spatio-temporal clusters and determine the impact of environmental factors and model the disease in order to better understand the spatio-temporal epidemiology of FMD in Iran. In this study, data related to the incidence of disease as well as environmental variables including meteorological variables, vegetation and topography of the region were used. This study was performed on 12442 cases of registered diseases in the years 1396-1387 in the city for the whole of Iran. First, general auto correlation indices including Moran'I index, General G index and Ripley's K index were used to investigate the distribution of the spatial pattern of the disease. Local Moran and the Gatis-Ord G* index were used to detect hot and cold spots. Then, with the help of the points of occurrence and time of the disease, spatial and spatio-temporal clusters of the disease were discovered. Spearman's correlation and linear regression analysis were used to evaluate the type and severity of correlation between environmental parameters and disease incidence. The results showed that the maximum incidence is in spring (44%) and the minimum incidence is in summer (15%). The months of Ordibehesht, Farvardin, Khordad and Dey, with a total of 6650 cases, were the most riskful months of the year, respectively. Autocorrelation tests showed the distribution of FMD clusters for every 10 years studied. The results of the local Moran index and the Gatis-Ord G* showed that the foci of this disease are still active in Iran and parts of Semnan, Tehran, Markazi, Fars, Lorestan, Khorasan Razavi and East Azerbaijan provinces are hot spots of FMD. In spatial scan, 8 clusters were identified, which confirmed the results of Moran and the Gatis-Ord G* index. In the spatio-temporal scan, 5 clusters were discovered that were consistent with the results of the spatial scan. Spearman analysis showed that there is a positive correlation between the incidence of the disease and vegetation, which is decreasing from the beginning to the end of the year and has a negative relationship in winter. Also, a positive and increasing correlation was observed with wind speed and precipitation from the beginning to the end of the year. In addition, a positive correlation between the incidence of the disease and altitude and a negative relationship with the direction of the slope was calculated. Linear regression analysis was also used to evaluate the effect of variables, which resulted in confirming Spearman correlation outputs. The results of the model showed that vegetation and topography are the most important environmental factors that affect the prevalence of snow fever in the region. The results of this study identify areas that are at higher risk and need more planning and attention to control the disease.