In the past decade, the rapid pace of development in mass spectrometry technologies has accelerated the rise of metabolomics and resulted in the generation of an unprecedented quantity of data. This marks an inexorable shift of the field into the realm of “big data,” necessitating the development of novel machine learning approaches capable of interpreting this information and integrating data from other “-omic” platforms such as genomics, transcriptomics, and proteomics. Leveraging artificial neural networks is the linchpin to achieving these goals. In particular, the development of novel neural network architectures capable of synthesizing data from both sequencing (genomics, transcriptomics) and mass spectrometry (metabolomics, proteomics) platforms, such as LSTM (long short-term memory) coupled with CNN (convolutional neural network) hybrid topologies, is crucial. The ability of neural networks to incorporate disparate information to perceive latent correlations is critical to successfully integrating the vast amount of existing data, including biochemical pathways and enzymatic substrate specificities, in next-generation computational metabolomics analysis pipelines.