Cognitive fatigue may have significant results if not intervened in factories, automobiles, and office environments. Development of a system for monitoring cognitive fatigue in real-life settings using unobtrusive wearable devices can help to minimize health problems, and work and car accidents. Photoplethysmography (PPG) sensors offer an unobtrusive way to track changes in heart rate and heart rate variability (HRV), which are indicative of cognitive fatigue levels. In this study, we propose a personalized PPG normalization technique to reduce inter-subject variability and enhance the performance of machine learning algorithms in classifying cognitive fatigue. The best-performing model, a Random Forest Classifier, achieved an accuracy of 80.5% in binary classification and demonstrated robust performance in regression tasks as well. The study highlights the potential of PPG-based wearables for non-obtrusive, long-term monitoring of cognitive fatigue, which could aid in preventing health issues associated with chronic fatigue.