An artificial neural network code is introduced for the prediction of a single-variable output in a fluid flow problem. Using opensource Python code, this neural network is optimally designed for the prediction of frictional loss through pipe bends, when a ground-truth simulation data is available for the model. The model is used to predict Darcy friction factor for a 90-degree and a 180-degree bend. Incompressible flow data used here is modeled using the Reynolds averaged Navier Stoke’s equations coupled with Reynolds stress model for the turbulence variables. Subsequently, the neural network is trained and tested. It is found to perform with a mean square error of 0.0023 and a mean average error of 0.0355, along with an association strength of 0.9964 between the input and output variables. When compared to an ensemble of metamodels, it is found to predict output more accurately by 25%. This is for input parameters such as Reynolds number between 10 and 0.1 million and curvature ratio between 0.01 and 0.2. With this, we hope that the method will aid in supplementing computational analyses with it's superior learning capabilities, and would reduce the computational cost for such problems.