During naturalistic information processing, individuals spontaneously segment their continuous experiences into discrete events, a phenomenon known as event segmentation. Traditional methods for assessing this process, which include subjective reports and neuroimaging techniques, often disrupt real-time segmentation or are costly and time-intensive. Our study investigated the potential of measuring event segmentation by recording and analyzing eye movements while participants viewed naturalistic videos. We collected eye movement data from healthy young adults as they watched commercial films (N = 104), or online Science, Technology, Engineering, and Mathematics (STEM) educational courses (N = 44). We analyzed changes in pupil size and eye movement speed near event boundaries and employed inter-subject correlation analysis (ISC) and hidden Markov models (HMM) to identify patterns indicative of event segmentation. We observed that both the speed of eye movements and pupil size dynamically responded to event boundaries, exhibiting heightened sensitivity to high-strength boundaries. Our analyses further revealed that event boundaries synchronized eye movements across participants. These boundaries can be effectively identified by HMM, yielding higher within-event similarity values and aligned with human-annotated boundaries. Importantly, HMM-based event segmentation metrics responded to experimental manipulations and predicted learning outcomes. This study provided a comprehensive computational framework for measuring event segmentation using eye-tracking. With the widespread accessibility of low-cost eye-tracking devices, the ability to measure event segmentation from eye movement data promises to deepen our understanding of this process in diverse real-world settings.