The aim of this study was to evaluate quality of life, sleep quality, depression level, and nutritional status in older adults using a videoconference-based method, and to examine the effects of these variables on quality of life through a multiple linear regression model. Eighty-four older adults aged 65 to 75 with a mean age of 69.85 ± 2.34 participated in the study. Evaluations of older adults were made using the videoconference method via smartphone. Quality of life of older adults was assessed with the Nottingham Health Profile, sleep quality with the Pittsburgh Sleep Quality Index, depression status with the Geriatric Depression Scale-Short Form, and nutritional status with the Mini Nutritional Assessment-Short Form. According to the videoconference-based assessments, 58.3% of older adults were found to have poor sleep quality, 40.5% exhibited moderate or higher levels of depressive symptoms, and 36.9% were identified as malnourished. Multiple linear regression analysis revealed that sleep quality, depression, and nutritional status together accounted for approximately 71.9% of the variance in quality of life (P < .001). Among these variables, depression level showed the strongest standardized effect on quality of life (Beta = .902), followed by nutritional status (Beta = -.463) and sleep quality (Beta = .440). In this study, assessments conducted via videoconference revealed a high prevalence of sleep disturbances, depressive symptoms, and malnutrition among older adults. The findings demonstrated that sleep quality, depression, and nutritional status had strong effects on quality of life. The high proportion of explained variance (71.9%) highlights the importance of these interrelated factors. Together, these 3 variables explained 71.9% of the variance in quality of life, highlighting the critical role of psychological and behavioral factors in understanding quality of life in older adults. Moreover, this study suggests that videoconference-based assessment may serve as a practical, accessible, and time-efficient approach for remotely evaluating key health-related parameters in the older population.