Road networks development and continual changes due to traffic and environmental factors, has a significant effect on accidents’ rate, which consequently cause casualties, financial losses and other problems. The purpose of this research study is to introduce an appropriate model for predicting accidents and evaluation the effects of rainfall and frost on road accident. Therefore, after collecting data in routs leading to Miyaneh city police station, each of the climate related factors effects was investigated and also the correlation between accidents occurrence and climate factor was analyzed. Then, the recorded collisions (injury, death and property damage) and the number of accidents during 1391-1393 in rainy and freezing condition, were modeled by neural network model. Also, the accidents number were predicted using Poisson model. Based on the relation between average temperature and the number of accidents, it was found that the number of accidents changed when the temperature rise, and generally in the cold months of year, because of the lowest traffic, the least number of accidents have been take place. The results showed that both Poisson and neural network models have a good ability to predict the total number of accidents. Artificial neural network had a better accuracy in accident prediction, which the R2 value in training and testing steps are about 98 and 96 percent, respectively. In Poisson model the R2 value for presented model and its validation were 53 and 64 percent, respectively. Also, statistical analysis showed that between observed and modeled number of accidents at the level of five percent, there is no significant difference.