Disease diagnosis through breath analysis have attracted a significant attention in recent years due to its non-invasive nature, rapid testing ability and applicability for the patients of all ages. More than 1000 volatile organic component (VOC) exists in human breath, but only a selected VOCs are associated with specific diseases. Selective identifications of those disease marker VOCs by using array of multiple sensors is highly desirable in the current scenario. Not only the use of efficient sensors but also the use of suitable classification algorithms is essential for the selective and reliable detection of those disease markers in the complex breath. In the current study, we fabricated noble metals (Au Pd and Pt) nanoparticles functionalized MoS₂ based sensor array for the selective identifications of different VOCs. Four sensors i.e. pure MoS₂, Au/MoS₂, Pd/MoS₂ and Pt/MoS₂ were tested in the exposure different VOCs like acetone, benzene, ethanol, xylene, 2-propenol, methanol and toluene at 50°C. Initially, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to discriminate those seven VOCs. As compared to the PCA, LDA was able to discriminate well among the seven VOCs. Four different machine learning algorithms like k-nearest neighbors (KNN), decision tree, random forest and multinomial logistic regression was used to identify those VOCs further. The classification accuracy of those seven VOCs by using KNN, decision tree, random forest and multinomial logistic regression were 97.14%, 92.43%, 84.1% and 98.97% respectively. These results authenticated that multinomial logistic regression performed best among all the four machine learning algorithms to discriminate and differentiate multiple VOCs popularly present in human breath.