This work demonstrates the potential of machine learning (ML) method to predict catalytic activity of transition metal complex precatalyst toward ethylene polymerization. For this purpose, 294 complexes and 15 molecular descriptors were selected to build the artificial neural network (ANN) model. The catalytic activity can be well predicted by the obtained ANN model, which was further validated by external complexes. Boruta algorithm was employed to explicitly decipher the importance of descriptors, illustrating the conjugated bond structure, and bulky substitutions are favorable for catalytic activity. The present work indicates that ML could give useful guidance for the new design of homogenous polyolefin catalyst. Machine learning method was applied to investigate the catalytic activity of 294 bis(imino)pyridine metal analogue complexes toward ethylene polymerization. By using 15 selected descriptors, the obtained neural network models exhibit good correlation and cross validation coefficient values, which were further validated by external complexes. The interpretation of descriptors indicates the important role of conjugated bond structure and bulky substitutions, providing guidance for the new design of homogenous polyolefin catalyst.