Salmonella spp. is a bacterial genus subject to constant health surveillance in poultry products' production and commercialization. Its presence may imply that meat and meat products cannot be commercialized, since it affects both slaughter animals and consumers' health. Traditional methods of isolation and identification of Salmonella spp. require at least five days for conclusive results. As an additional factor, changes in the bacterial cell surface's antigen expression can lead to false-negative results in traditional serotyping techniques. Analytical techniques for rapid detection and quantification of pathogens contribute to keeping Brazil in a prominent poultry production position. Metabolomics is an analytical tool used to study small molecules from the metabolic profile of cells, tissues, or fluids metabolic profile. Such molecules, called biomarkers, are fundamental in clinical practice to determine disease status. Thus, metabolomics is useful for prospecting and detecting markers of pathogens such as Salmonella spp. in poultry production. The present project aims to map samples' metabolic profile from carcasses of chickens contaminated with Salmonella spp. and samples from carcasses free from contamination. The data collected through metabolomics by mass spectrometry will integrate a computer system based on artificial intelligence by machine learning to obtain parameters for diagnosis and prognosis of salmonellosis in chickens quickly, still in the slaughter process. This initiative estimates that it will meet the need to detect and quantify possible pathogens in a phase before the product's commercialization. With the results generated, it will be possible to provide a quick method of detecting Salmonella in the chicken carcass even on the same day it is slaughtered.