Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has become a staple in clinical microbiology laboratories. Protein-profiling of bacteria using this technique has accelerated the identification of pathogens in diagnostic workflows. Recently, lipid profiling has emerged as a way to complement bacterial identification where protein-based methods fail to provide accurate results. This study aimed to address the challenge of rapid discrimination between Escherichia coli and Shigella spp. using MALDI-TOF MS in the negative ion mode for lipid profiling coupled with machine learning. Both E. coli and Shigella species are closely related; they share high sequence homology, reported for 16S rRNA gene sequence similarities between E. coli and Shigella spp. exceeding 99%, and a similar protein expression pattern but are epidemiologically distinct. A bacterial collection of 45 E. coli, 48 Shigella flexneri, and 62 Shigella sonnei clinical isolates were submitted to lipid profiling in negative ion mode using the MALDI Biotyper Sirius® system after treatment with mild-acid hydrolysis (acetic acid 1% v/v for 15 min at 98°C). Spectra were then analyzed using our in-house machine learning algorithm and top-ranked features used for the discrimination of the bacterial species. Here, as a proof-of-concept, we showed that lipid profiling might have the potential to differentiate E. coli from Shigella species using the analysis of the top five ranked features obtained by MALDI-TOF MS in the negative ion mode of the MALDI Biotyper Sirius® system. Based on this new approach, MALDI-TOF MS analysis of lipids might help pave the way toward these goals.