Milk's biological origin determination, including its adulteration and authenticity, presents serious limitations, highlighting the need for innovative advanced solutions. The utilisation of proteomic technologies combined with personalised algorithms creates great potential for a more comprehensive approach to analysing milk samples effectively. The current study presents an innovative approach utilising proteomics and neural networks to classify and distinguish bovine, ovine and caprine milk samples by employing advanced machine learning techniques; we developed a precise and reliable model capable of distinguishing the unique mass spectral signatures associated with each species. Our dataset includes a diverse range of mass spectra collected from milk samples after MALDI-TOF MS (Matrix-assisted laser desorption/ionization-time of flight mass spectrometry) analysis, which were used to train, validate, and test the neural network model. The results indicate a high level of accuracy in species identification, underscoring the model's potential applications in dairy product authentication, quality assurance, and food safety. The current research offers a significant contribution to agricultural science, providing a cutting-edge method for species-specific classification through mass spectrometry. The dataset comprises 648, 1554, and 2392 spectra, represented by 16,018, 38,394, and 55,055 eight-dimensional vectors from bovine, caprine, and ovine milk, respectively.