Drowsy drivers contribute to high rates of road accidents, leading to numerous fatalities and injuries. The dangers of drowsiness extend beyond the roads, affecting workplaces that require continuous attention and focus. This work aims to develop a non-intrusive, real-time system capable of assessing the user’s drowsiness level by processing physiological signals collected by smartwatch sensors. The study utilized photoplethysmography (PPG) and heart rate data from a smartwatch, labeled with an adapted Karolinska Sleepiness Scale (KSS). Deep Learning techniques were used to fine-tune hyperparameters and train a 1-dimensional Convolutional Neural Network to reach better prediction performance on drowsiness data. Besides, a 5-fold cross-validation technique was used to evaluate performance and create an ensemble, enhancing prediction robustness through election method. The results show significant prediction accuracy using the ensemble on test data (91.41%). The final pre-trained model was integrated into the smartwatch, creating a real-time drowsiness detection system. This system alerts the user through sound and vibration feedback, preventing them from falling asleep and reducing the risk of accidents.