Abstract: Automatic mesh generation and adaptation are bottleneck problems restricting computational fluid dynamics (CFD). Grid quality, efficiency, flexibility, automation level, and robustness are several key issues in grid generation. Mesh size control is significant in unstructured mesh generation which directly impacts the mesh quality, efficiency, and solution accuracy. Controlling mesh size by the background grid method requires mesh size defined on a background mesh by solving differential equations and interpolating from background mesh to specific location, which is very tedious and time-consuming in traditional unstructured grid generation. In this paper, two novel mesh size control methods are proposed in terms of efficiency and automation level. Firstly, radial basis function (RBF) interpolation was developed to control mesh size. In order to improve the efficiency of RBF interpolation, the greedy algorithm was applied to reduce the list of reference nodes. Meanwhile, an artificial neural network (ANN) is used to control the mesh size, relative wall distance, and relative mesh size are introduced as input and output parameters for the ANN. Training models are established and samples (2D cylinder and airfoil grids) are generated by commercial software. The relationship is established between wall distance and mesh size by machine learning. Several meshes are generated with the aforementioned three methods, the results demonstrate that the RBF method and the ANN method are 5-10 times more efficient than the background mesh method, which contributes to efficiency improvement of the grid generation process. Finally, the ANN method is extended to mesh size control of anisotropic hybrid grids, which also obtained meshes of good quality.