Reconstruction of traffic accidents has been so crucial scientific process in order to make impartial and judicious decisions. This study focuses on impact speed prediction of accident sufferers just before the collision in a comprehensive scientific way by using an accident reconstruction software called "vCrash" and Function Fitting Neural Network (FITNET) artificial intelligence method (predictor) in case of absence of skid marks or other clues about calculating impact speeds. A sample real world accident was simulated on the software several times by changing collision speeds to form different deformation on the collision regions of the vehicles in every simulation. Every single deformation amount corresponding to each impact velocity was recorded and used as teaching data for FITNET prediction model. Using 10-fold cross validation, mean squared error (MSE) and multiple correlation coefficients (R) were observed to exhibit performance of the prediction model. The model performed high R (close to 1) and acceptable MSE values. This method aims that, in a probable similar accident scene in future, it will be possible to analyze the impact speeds just by entering average deformation amounts into an application on a portable device at the accident scene without requirement of expensive reconstruction tools and it will be a guide for analysis of other accident types.