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Accurate detection of exhaled acetone (ACE) and isopropanol (IPA) mixtures is critical for the non-invasive screening of diabetic ketoacidosis and the continuous monitoring of lipid metabolism in type 1 diabetes mellitus. However, the broad clinical application of this approach remains severely constrained by the inherent cross-sensitivity of metal oxide semiconductor (MOS) sensors. To address this, a microelectromechanical systems (MEMS) dual-sensor array integrating PdO-modified SnO2/ZnO and MoS2/ZIF-67 nanocomposites was developed for simultaneous gas identification and concentration regression. By pioneering the extraction of distinct transient thermokinetic responses of the materials, combined with a multi-task learning architecture featuring a 1D Convolutional Neural Network and a Bidirectional Long Short-Term Memory (CNN-BiLSTM) network, the system efficiently analyzed dynamic sensing data using a sliding time window (K). At an optimal 15-s window, the model demonstrated robust real-time predictive capabilities, achieving 95.4% classification accuracy and a regression mean absolute error (MAE) of 0.625 ppm across clinical IPA/ACE ratios. This deep learning-enabled framework circumvents traditional steady-state limitations, providing a scalable, low-power solution for precise clinical breathomics and metabolic disease screening.