Middle-aged women experience various physical and psychological challenges which can ultimately deteriorate their life satisfaction. Therefore, monitoring and caring for their life satisfaction and subjective well-being is essential. Digital phenotype, the representation of individuals’ health status based on data collected from digital devices, is a valuable tool for monitoring their health status. However, research on digital phenotypes for detecting middle-aged women’s life satisfaction remains underexplored. This exploratory study investigated meaningful health-related features for inferring life satisfaction among middle-aged women, while verifying their predictive abilities using a machine learning approach. We explored features reflecting individuals’ psychological health and daily life patterns, utilizing data collected from smartphones and Fitbits. Our findings identified several meaningful health-related features across the dimensions of psychological and physical health, social interactions, and sleep and meal patterns. We also suggest practical implications for smartphone applications and wearable devices aimed at monitoring life satisfaction and subjective well-being among middle-aged women.