Discrimination of gas mixtures remains a challenging task due to overlapping sensor responses and pronounced temporal evolution under real-time monitoring. To address this challenge, we investigate deep learning-based approaches for the analysis of C O-C2 H4 gas mixtures using time-series signals acquired from 16-channel metal-oxide sensor (MOS) array under 12h real-time monitoring conditions. Convolutional, recurrent, and hybrid architectures, including CNN, GRU, LSTM, TCN, and their combinations, are evaluated for classification and regression multitasking. The results demonstrate that the GRUbased model consistently achieves the best overall performance, with a classification accuracy of 96.06% and a global regression R2 of 0.939. In contrast, hybrid architectures incorporating additional convolutional modules, such as CNN+GRU and cascaded TCN+GRU, do not provide further performance improvements, suggesting that the essential predictive information is primarily encoded in global temporal dynamics. Therefore, our study provides valuable insights for a robust and lightweight AI-driven readout system for the next-generation real-time monitoring device.