In this study, we present the design and development of a portable electronic nose (E-nose) system for detecting and classifying spoiled household food through the application of machine learning (ML) techniques. The targeted odors include fungi from bread, spoiled rice, spoiled milk, yoghurt, rotten egg, rotten boiled egg, rotten pork, and rotten beef, totaling eight odor classes. A total of 1800 samples were collected using three gas sensors and one temperature sensor. After outlier removal with Isolation Forest, 1000 samples remained. Multiple ML models were trained and evaluated over ten iterations, comparing classification accuracy and processing time. Among all the models, the k-nearest neighbor (KNN) achieved the highest performance, with an average accuracy of 99.889% and an average processing time of 0.167477 s. The decision tree (DT) model followed closely with an accuracy of 99.843% and required a significantly less processing time of 0.012080 s. Although DT has a slightly lower accuracy than KNN, its processing time is 13.86 times faster. For our scenario that requires real-time results, DT is a better choice than KNN. The proposed portable E-nose demonstrates strong potential for real-world applications such as food spoilage detection, environmental monitoring, and health diagnostics.