<p>Differences in microbial communities have been observed across various skin sites, such as dry, moist, and sebaceous areas. These skin types influence the diversity of microbials present in each microenvironment. Commonly found skin microbes include <i>Staphylococcus epidermidis</i>, <i>Cutibacterium acnes</i> and <i>Corynebacterium</i> sp. Ethnicity, age, gender and health status are a few individual-specific factors that shape the skin microbiome. Every individual retains unique and distinct skin microbial communities despite constant exposure to environmental changes. In forensic investigations, human identification can be achieved through skin microbial trace analysis left behind on surfaces and objects. Temporal stability of the microbial profile, on skin, for up to two weeks, is an attractive feature for the implementation of skin microbiome analysis in forensic applications. Additionally, microbial traces can assist in determining geolocation and estimating postmortem interval. Although high-throughput sequencing technologies have accelerated microbiome research and provide species-level information, the skin is a low-biomass sample, and there are currently no standardised protocols from sample collection to analysis. Machine learning is rapidly advancing skin microbiome research by enabling the analysis of large and complex datasets to uncover patterns. These patterns can be used for predicting skin health conditions, matching skin samples to specific microenvironments, identifying individuals and inferring biogeographic origins. The present review highlights current research in the application of skin microbiome analysis for forensics and future potential applications for age and gender determination. Additionally, the factors affecting the skin microbiome diversity are discussed. Skin microbiome research will accelerate enrichment of microbiome databases, which could complement the standard STR typing in accurate human identification.</p>