Tea and herbal infusions are the most consumed non-alcoholic beverages worldwide and possess bioactive components with multiple health benefits. They are categorized into different classes that depend on their elaboration process, origin, and components. Commonly, analytical methods are employed to classify tea according to its chemical composition using liquid and gas chromatography–mass spectrometry, among others. Novel methods, such as electronic noses (e-noses), effectively provide real-time and objective monitoring of odors for extended periods of time. This work aimed to classify eight different types of tea (green, white, black, spearmint, mint, hibiscus, lemongrass, and chamomile) using two feature extraction methods and two pattern recognition analyses that were compared. A total of 34 tea samples were analyzed using an e-nose consisting of an olfactometer as a sample-handling system, seven chemo-resistive gas sensors, and a 12-bit analog-to-digital converter. Tea samples were measured 10 times to ensure repeatability, resulting in a database of 340 tea measures with 2499 samples each per sensor. Data were preprocessed using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The information extracted was classified using an Artificial Neural Network (ANN) and k-nearest neighbor (k–NN). The best architecture in ANN and distance in k-NN were demonstrated through 10 k-fold cross-validation. The classification rate was 93% in ANN and PCA, 73% in ANN and PARAFAC, 94% in k-NN and PCA, and 84% in k-NN and PARAFAC. This demonstrates that conventional PCA is better than complex PARAFAC. Our findings not only contribute to the field of tea and herbal infusion classification but also underscore the potential of e-nose systems for discriminating between diverse tea types and herbal infusions based on their odor profiles.