Anthrax disease, caused by Bacillus anthracis, is a zoonotic disease with significant epidemiological implications. This study demonstrates the application of machine learning algorithms and MALDI-TOF mass spectrometry for rapid identification and profiling of B. anthracis and the related species Bacillus cereus in Argentina. Our results validate the efficacy of these techniques in creating a local database of peptide fingerprints, providing a foundation for robust surveillance and diagnosis in public health laboratories. Statistical analyses confirm species-specific biomarkers, supporting the development of accessible screening protocols. This approach highlights the potential for MALDI-TOF MS in anthrax diagnostics and lays groundwork for future expansions in pathogen profiling.