Eye movement patterns have become an essential element in modern approaches for identifying air traffic controller fatigue. By observing eye movements among various individuals and environments, researchers have discovered correlations with multiple physiological metrics and cognitive processing abilities. This study involved human-in-the-loop simulations to collect eye movement and fatigue data from air traffic controllers and students. The eye movements were classified into three main types: fixation, saccade, and blink. Statistical analyses were performed to determine the most important indicators. Using support vector machine and random forest models for training and prediction, it was found that the fixation characteristic is significantly important for monitoring air traffic controller fatigue. The implementation of this model has the potential to identify forthcoming instances of controller fatigue during their shifts, thereby helping to avert the possibility of unsafe situations.