Since the famously inconclusive Viking missions, we have observed an increased desire to discover life outside the Earth. However, if we are to plan effective life-detection missions, then we must meet the challenge of classifying potential “agnostic” biosignatures (indicators of life or the absence of life). Agnostic refers to attempting to not use biosignatures that would bias towards Earth centric life standards, which would be “putting the answer in the question.” Machine learning techniques, specifically statistical classification already showed promising results in other fields. Applied to astrobiology, it may provide clarity on how different and independent measurements of the same biosignature affects your confidence in whether it is indicative of life. In this work, these algorithms were implemented to classify Raman spectra of potential biosignatures. Data was collected from public databases and individual research papers, processed, and then evaluated with several different algorithms. After thousands of simulations to allow the algorithms to test their classifications, we observed an 81% probability of correct classification when all the algorithms’ individual predictions were combined. These results demonstrate Raman spectroscopy’s potential for life-detection missions, and ability to improve upon a qualitative criterion for identifying indicative of life biosignatures.