Binary classifiers are not optimal for a large number of classes and are sensitive to class imbalances during training resulting in problems to identify all functional groups in a molecule or mixtures . In this work, we present the first ML method, to our knowledge, that integrates FTIR and MS data to obtain a combined set of features as a multi-class, multi-label classification methodology. Our method predicts multiple functional groups for a given molecule in a database-free manner, as compared to identifying a molecule through peak matching or only identifying the major functional group in the molecule (Figure ).