The most recent studies demonstrate the predictive power of neural-networks. Used neural-networks were success to predict of economic results trends, and the neural-networks have an advantage, whereas its can the nonlinear functions approximate. In doing so, it can provide the alternative analysis regression of the biology of modeling growth. Some Few searches were conducted by neural-networks on the animal growth model artificially. The present study was conducted to compare, the parameters of various associated input performance. For example, the chickens price/kg in poultry farm, the price/tons of poultry feed, the quantity import of white meat, and the numbers of chickens slaughtered, which is expected to be of help in future for our study. We applied series of data/monthly for rates exchange in between 2009 to 2014. Aims of recent study in the first place; to develop ANN- based models to study the Fate of the Poultry Industry Forecasting in Kurdistan Region. Secondly, through previous data we can recommend that, neural-networks method, more suitable in chicken industries, than the simple analysis regression, if accurate data are collected and processed in such forms.