Driver monitoring plays a critical role in intelligent transportation systems, yet detecting fatigue, alcohol impairment, and distraction remains highly challenging due to their subtle and overlapping cues. A major obstacle is the scarcity of annotated alcohol-related data, which limits the training of robust classifiers. This work presents a practical framework that combines adversarial data augmentation with lightweight deep learning for unified driver state recognition. A CycleGAN-based pipeline synthesizes alcohol-impaired facial images from fatigue samples, introducing physiologically motivated effects such as skin flushing, gaze irregularities, and periocular redness. For classification, a MobileNetV2 backbone enhanced with Squeeze-and-Excitation (SE) attention adaptively emphasizes critical channels while remaining computationally efficient. Evaluated on a curated seven-class dataset, the model achieves 97.67% accuracy with a test loss of 0.0655, confirming the viability of adversarial augmentation coupled with lightweight CNNs for real-time driver monitoring.