A high-performance ZnO nanowire-based e-nose using a multiple sensor array comprising four sensors has been demonstrated for its ability to successfully identify and classify four gases, namely CO2, H2S, NH3, and NO2, from their mixture. For this, a combination of algorithms, namely principal component analysis (PCA), linear discriminant analysis (LDA), a support vector machine (SVM), and decision forest (DF) have been systematically utilized. A data repository was created using response curves, and the five extracted variables, namely sensor response, response time, area under the response curve, response slope, and concentration of gases. PCA analysis indicated clustering of the gases, having a variance of 66.39%, 33.01%, and 0.05% for the first three components, respectively. LDA has been successfully used to classify the gases, employing the approach of maximization of between-class variance. The SVM classifier with a radial basis function (RBF) kernel model showed the highest accuracy of 98.13% and 82.50% on the training data and the validation data, respectively. The DF classifier generated a tree in which four toxic gases were successfully classified. The confusion matrix showed a 0% out of box estimate error rate for the training dataset and a 2.5% test error rate for the validation dataset. The SVM and the DF classifier models demonstrated an excellent classification accuracy.