The study and control of the airflow in the indoor environment is crucial. Computational fluid dynamics (CFD) allows detailed flow analysis, but faces challenges mainly in terms of computational efficiency. Modern data driven tools have lately received much attention for inexpensive airflow simulations. In this work we develop a coupled CFD–deep learning framework by employing a neural network to replace a classical zero-equation eddy viscosity turbulence model. A standard multi-layer perceptron is trained on TensorFlow and subsequently transferred and applied to a CFD solver in OpenFOAM. Training and testing were performed by collecting data from validated Reynolds-averaged Navier–Stokes (RANS) simulations of indoor airflow from literature. The new coupled framework enables accurate results with significantly faster prediction speed. A primary challenge remains in being able to train the neural network over a larger dataset and provide higher generalizability to the model with sufficient accuracy.