The present study proposes a new approach based on artificial neural network (ANN) for predicting thermodynamic parameters in an indoor environment. The proposed prediction approach consists of two independently trained ANN models, where some output variables from the previous model are used as inputs to the subsequent model. To obtain the target variables, which are pressure and temperature distribution, the velocity distribution is defined as the influencing variable that has a significant impact on the target variables. The predicted velocity distribution from the first model is employed as an additional input for the second model. The training, validation, and testing datasets are obtained from the results of computational fluid dynamics (CFD) analysis, and it contains a total of 100 case scenarios. To evaluate the accuracy of the prediction model, several performance indices are utilized, such as R2 and root mean square, etc., which are based on mathematical statistics methods. The prediction results show that adding variables affecting the output value, such as the velocity, to the input node improves the performance of the model. That is, the proposed approach outperforms existing ANN models and provides a reasonable solution for indoor airflow prediction.