<p>Human and animal health rely on balancing cell division and cell death to maintain normal homeostasis. This process is accomplished by regulated cell death (RCD), whose imbalance can lead to disease. Currently, the most frequently used method for analyzing RCD is fluorescence microscopy. This method has limitations and potential side effects due to the presence of fluorescent labels. Furthermore, fluorescence often lacks specificity and may have side effects. In the quest to overcome such difficulties, label-free approaches have come into focus.Here, Raman microscopy in combination with machine learning is used to investigate RCDs, where biochemical molecular “fingerprints” are investigated with a focus on the vibrations of atoms in molecules. Three different and unique RCD types with different genetic and biochemical machinery, namely, ferroptosis is studied in comparison with apoptosis, and necroptosis in the murine fibroblast line L929sAhFas. Interestingly, during ferroptosis, a decrease in the wavenumber at 939 cm^-1 was observed, which is associated with a potential reduction in the expression of collagen – a compound essential in multiple diseases. Data analysis was performed by machine learning (ML), here SVMs, where the model utilizing the spectra directly into a support vector machine (SVM) outperforms other SVM strategies correctly predicting 73% of all spectra. Other methods: PCA-SVM (principal component analysis-SVM), peak fitting-AUC-SVM (area under the curve) and peak fitting-spectral reconstruction-SVM rendered prediction accuracies of ~52%, ~43%, and 61%, respectively. Peak fitting has the additional benefit of enabling the biological interpretation of Raman scattering peaks by using the area under the curve, although at a loss of general accuracy. The potential of Raman microscopy in biology, in combination with machine learning pipelines, can be applied to a broader field of cell biology, not limited to regulated cell death.</p>