Higher demands on skin care cosmetic products for strong performance drive intense research to understand the mechanisms of skin aging and design strategies to improve overall skin health. Today we know that our needs and influencers of skin health and skin aging change throughout our life journey due to both extrinsic factors, such as environmental factors and lifestyle factors, as well as our intrinsic factors. Furthermore, we need to consider our microflora, a collection of micro-organisms such as bacteria, viruses, and fungi, which is a living ecosystem in our gut and on our skin, that can have a major impact on our health. Here, we are viewing a holistic approach to understand the collective effect of the key influencers of skin health and skin aging both reviewing how each of them impact the skin, but more importantly to identify molecular conjunction pathways of these different factors in order to get a better understanding of the integrated “genome-microbiome-exposome” effect. For this purpose and in order to translate molecularly the impact of the key influencers of skin health and skin aging, we built a digital model based on system biology using different bioinformatics tools. This model is considering both the positive and negative impact of our genome (genes, age/gender), exposome: external (sun, pollution, climate) and lifestyle factors (sleep, stress, exercise, nutrition, skin care routine), as well as the role of our skin microbiome, and allowed us in a first application to evaluate the effect of the genome in the synthesis of collagen in the skin and the determination of a suitable target for boosting pro-collagen synthesis. In conclusion, we have, through our digital holistic approach, defined the skin interactome concept, as an advanced tool to better understand the molecular genesis of skin aging and further develop a strategy to balance the influence of the exposome and microbiome to protect, prevent, and delay the appearance of skin aging signs and preserve good skin health condition. In addition, this model will aid in identifying and optimizing skin treatment options based on external triggers, as well as helping to design optimal treatments modulating the intrinsic pathways.