Ensuring food safety requires continuous innovation, especially in the detection of foodborne pathogens and chemical contaminants. In this study, we present a system that combines Raman spectroscopy with machine learning (ML) algorithms for the precise detection and analysis of VOCs linked to foodborne pathogens in complex liquid mixtures. A remote fiber-optic Raman probe was developed to collect spectral data from 42 distinct VOC mixtures, representing contamination scenarios with dilution levels ranging from undiluted to highly diluted states. A dataset comprising 1445 Raman spectra was analyzed using classification and regression ML models, including multi-layer perceptron (MLP), random forest, and extreme gradient boosting decision trees (XGBDT). The optimized ML models achieved over 90% classification accuracy for pure VOCs and demonstrated robust performance in identifying mixtures containing up to six VOCs at concentrations as low as 0.25% (400-fold dilution). Additionally, regression analysis effectively predicted VOC concentrations at levels as low as 1% (100-fold dilution), with the best model achieving an