Mental health concerns have garnered increasing attention, highlighting the importance of timely and accurate identification of individual stress states as a critical research domain. This study employs the multimodal StressID dataset to evaluate the contributions of three modalities—physiological signals, video, and audio—in stress recognition tasks. A set of machine learning models, including Random Forests (RF), Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP), and K-Nearest Neighbors (KNN), were trained and tested with optimized parameters for each modality. In addition, the effectiveness of different multimodal fusion strategies was systematically examined. The unimodal experiments revealed that the physiological modality achieved the highest performance in the binary stress classification task (F1-score = 0.751), whereas the audio modality outperformed the others in the three-class classification task (F1-score = 0.625). In the multimodal setting, feature-level fusion yielded stable improvements in the binary classification task, while decision-level fusion achieved superior performance in the three-class classification task (F1-score = 0.65). These findings demonstrate that multimodal integration can substantially enhance the accuracy of stress recognition. Future research directions include incorporating temporal modeling and addressing data imbalance to further improve the robustness and applicability of stress recognition systems.