Customers have used various skin care and functional cosmetics to prevent their skin aging as well as improve current condition of the skin. It is important to analyzing and understanding their skin to select optimized skin care solutions. The aim of this study is to quantitatively analyze the effect of environment, lifestyle, and innate genes on the current skin condition. For this purpose, degree of hydration, sebum, wrinkles, melanin, dullness and redness in a highly controlled condition were collected in conjunction with a questionnaire survey analyzing their lifestyles and genetic data from about 3000 women. We classified participants based on the types of skin, using 6 kinds of index representing skin properties, questionnaire survey on lifestyle and genetic data with Gaussian Mixture Model and Decision Tree for classification model. Through this study, it was possible to divide the skin of Korean women into 12 clusters according to wrinkles, melanin, redness, dullness and oil/moisture balance with Gaussian Mixture Model. Also, we were able to identify a pattern in which each factor was correlated. Next, we tried to discover factors for predicting skin condition changes through correlation analysis with lifestyle, climate/environment, and innate genes that affect the current skin condition. Ultimate purpose of this study is to predict future skin condition using big data and AI technology. This knowledge will enable us to provide more proactive and personalized solutions not only for cosmetics but also for life care such as lifestyle, eating habits, and environmental response.