In this paper an approach is presented to predict the concentration and the trend of seismic pattern and clusters of earthquakes. The method is based on Copulas and artificial Neural Networks that have attracted much attention in spatial statistics over the past few years. They are used as a flexible alternative to traditional methods for non- Gaussian spatial modeling and interpolation. This methodology shows how it can be predicted aftershocks distribution in a Bayesian framework by assigning priors to all model parameters. The Gaussian spatial copula model is equivalent to trans-Gaussian kriging with transformation function. A restriction of the Gaussian copula is that it models not only a symmetric but even a radials symmetric dependence, where high and low quartiles have equal dependence properties. Experimental results show that the proposed models are superior to predict and identify seismic risk at high seismicity areas.