Foodborne pathogens such as Bacillus cereus, Staphylococcus aureus, and Escherichia coli are major causes of gastrointestinal diseases worldwide and frequently contaminate dairy products. Compared to nucleic acid detection and MALDI-TOF MS, hyperspectral imaging (HSI) offering advantages such as multiple bands, rapid, minimal damage, non-contact, and non-destructive detection. However, current HSI methods require agar plate cultures, which are time-consuming and labor-intensive. This study is the first to use bacterial broth in a 24-well plate to collect HSI spectra, combined with machine learning for enhanced feature extraction and classification. After data augmentation and dimensionality reduction via principal component analysis (PCA) and linear discriminant analysis (LDA), deep neural networks and support vector machines (DNN-SVM) resulted in prediction accuracies of 100 % on the training set, 98.31 % on the testing set, and 93.33 % on the validation set for classifying B. cereus, E. coli, and S. aureus. As a result, a high-throughput, rapid, and non-destructive detection method was developed, which is expected to detect 24 bacterial broth samples in less than ten minutes. It indicates the potential of HSI to be used as a feasible, robust, and non-destructive solution for real-time monitoring of microbial pathogens in food.