With the advancement of artificial intelligence in recent years, an increasing number of studies have used machine-learning models to improve the sensitivity and accuracy of smart electronic noses. However, given that multiple machine learning models are available, it is important to understand the advantages of each model in enhancing smart electronic noses. Furthermore, data from smart electronic noses must be properly processed and statistically analyzed to extract their characteristics. These procedures play a vital role in the success of machine learning models. In this study, we examined multiple machine learning models ranging from traditional models, such as Random Forest, Support Vector Classification and Multi-layer Perceptron, to deep neural networks, such as Artificial Neural Networks, to predict the presence of gases in a mixture. A standardized process for processing raw data and analyzing smart electronic nose data is also proposed. The study was conducted on a dataset of three gas mixtures (Ethanol, Acetone and Methanol) with 135 unique combinations of eight classes fed into an electronic nose. In total, more than 12,800 data points were recorded in the dataset. By performing the proposed process on the datasets and testing all four machine learning models, the effectiveness of these models can be compared. The study found that the Support Vector Machine model achieved the best accuracy of 94%, while other models, such as Random Forest with accuracy of 93%, customize Artificial Neural Networks and Multi-Layers Perceptron also demonstrated it can be used to achieve a reasonable accuracy of 86% and 88% correspondingly.