This study explores the use of Bayesian penalized regression models in analyzing high-dimensional Raman spectroscopy data for disease detection, showcasing superior accuracy compared to traditional machine learning methods. The findings introduce a groundbreaking approach that revolutionizes disease diagnosis by leveraging Bayesian analysis and shrinkage priors, enabling more precise and effective identification of infections in saliva or blood serum samples.