Mental stress adversely impacts both physical and mental health, with chronic stress leading to serious health concerns. Photoplethysmography (PPG) sensors, widely available in wearable devices, offer a convenient, cost-effective, and non-invasive method for stress monitoring. This study proposes a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) hybrid architecture for real-time stress detection using just PPG signals. Trained and validated on the publicly available WESAD dataset, the model achieves exceptional performance metrics: 97.90\% accuracy, 98.30\% specificity, 97.20\% sensitivity, 97.06\% F1-score, 99.12\% AUC, and 95.42\% Cohen's kappa. The lightweight model exhibits high accuracy in stress detection while maintaining computational efficiency, making it particularly suitable for wearable devices. These results highlight the potential of this approach for practical real-time stress monitoring and management applications.