Metallic glasses (MGs) are widely used in various fields due to their superior physical properties. Glass-forming ability (GFA) represents the difficulty of forming MGs. Therefore, understanding and establishing the connection between materials characteristics and GFA is a great challenge in MGs research. In this work, to generate a new criterion to characterize GFA, symbolic regression and artificial neural network (ANN) were employed built on 7795 pieces of data. A completely new criterion was proposed and revealed the relationship between three characteristic temperatures (wherein T_g is the glass transition temperature, T_x is the onset crystallization temperature, and T_l is the liquidus temperature) and GFA. The new criterion not only exhibits a higher correlation to the critical casting diameter (D_max) than the other 11 reported criteria but also illustrates the importance of high power (T_x − T_g)/(T_l − T_x) in characterizing GFA. Moreover, to test the criterion on unreported data, three models that can, respectively, perform GFA classification, predict D_max, and three characteristic temperatures were built through artificial neural networks. Then, 439 new data generated by the ANN model were generated by models applied on Zr–Co–Al–X (X = W, Si, and Ni) alloys. On the testing data, the new criterion shows stronger generalization than other criteria, which proves its reliability and effectiveness.