A machine learning BERT transformer model, namely, ChemMLM was developed to learn from the semantic meaning of the reaction SMILES strings, from the complete reaction dataset identified from the last step. Using the masked language model scheme, analogous to missing words in sentences, ChemMLM was trained to predict the possible missing molecules for the incomplete reactions. From the test dataset, it is proven that ChemMLM was confident to predict short and middle (token length<=10) length missing molecules. Given the classification of LHS or RHS species insufficient of the incomplete reactions, ChemMLM either predicted missing molecules to the LHS or RHS. ChemMLM further completed 34.8% and 16.8% reactions from the total USPTO and Reaxys ® reaction dataset. The entire workflow completed 52.4% and 60.9% of reactions from USPTO and Reaxys ® in total, retaining with false positive complete reactions that could be only manually detected.