As the global population ages, mental health issues such as depression, anxiety, and insomnia have become more prevalent among older adults, significantly impacting their quality of life and social participation. Contactless mental health assessment methods have gained attention in recent years, offering objective diagnostic tools and enabling remote care to improve the accessibility and quality of mental healthcare. Eye movement analysis, in particular, has shown promise for emotion recognition and mental state assessment, with previous studies highlighting gaze patterns as indicators of depression and anxiety. However, most research on eye movements and mental health has focused on the general population, with limited exploration of how gaze behaviors in older adults are related to mental health conditions. To address this gap, our study investigates the relationship between specific gaze patterns and depression, anxiety, and insomnia in older adults. Data were collected from 50 older adults aged 60 to 75 through an interactive digital platform, with facial videos recorded during their responses to mental health-related questions. The study classified eye movement behaviors into two categories: “Atypical Interaction Group” (Sustained Gaze Avoidance and Sustained Vacant Stare) and “Typical Interaction Group” (Closed or Deep Blinking and Brief Glances Away). Statistical analysis revealed that the Atypical Interaction Group exhibited significantly higher depression, anxiety, and insomnia scores, suggesting that specific gaze features may signal higher mental health risks. The findings underscore the potential of gaze analysis for contactless mental health screening and early intervention in older adults, offering a pathway for future research into automated annotation methods and validation across larger and more diverse populations.