Including uncertainties such as the performance of the teams, player performance indicators, and the quality of the competitors, there are numerous factors affecting the result of a game. Therefore, prediction of the game results is quite a complicated and a conspicuous research problem. Various artificial intelligence models were developed in order to solve this problem. By drawing together the advantageous sides of various artificial methods, this study aims to develop a hybrid intelligent system in order to better predict the result of a basketball game. Firstly, a prediction model was developed via artificial neural network (ANN), which is frequently used in game result predictions. The success of this developed ANN model in predicting the result of the game was 70.8%. In order to increase this success rate, a new concurrent neuro fuzzy system (CNFS) was suggested which was combined with fuzzy logic system that determined whether the team was favorite. The accurate prediction rate increased to 79.2% via this suggested CNFS model. Moreover, the results of the models developed were compared with each other and previous studies predicting the game results. As the conclusion of the comparisons, it was observed that CNFS model had a remarkable talent in predicting the game results.