The human body is mechanically unstable, while the brain as the main controller, is responsible to maintain our balance. However, the mechanisms of the brain towards balancing are still an open research question and thus in this article, we propose a novel modeling architecture for replicating and understanding the fundamental mechanisms for generating balance in the humans. Towards this aim, a nonlinear Recurrent Neural Network (RNN) has been proposed and trained that has the ability to predict the performance of the Central Nervous System (CNS) in stabilizing the human body with high accuracy and that has been trained based on multiple collected human based balancing data and by utilizing system identification techniques. One fundamental contribution of the article is the fact that the obtained network, for the balancing mechanisms, is experimentally evaluated on a single link inverted pendulum that replicates the basic model of the human balance and can be directly extended in the area of humanoids and balancing exoskeletons.