Conservation biology requires accurate data on how human-induced threats affect wildlife fitness and survival. Gut microbiota play a critical role in health by influencing physiology, nutrition, immunology, and behaviour. Advances in non-invasive sampling, particularly scat microbiome analysis, offer scalable conservation solutions. This study establishes a benchmark using basic machine learning algorithms (SVM, Ranger, glmnet, and xgboost) to predict health outcomes in koalas from non-invasive scat microbiome data. Scat samples from 125 koalas were analysed using 16S PacBio HiFi sequencing. By incorporating a phylogenetic approach and integrating additional metrics such as sex, age, and stress metabolites, which can potentially be acquired non-invasively, we achieved high accuracy in predicting key health outcomes, including body condition score (BCS), disease status, survival outcome, and weight. The algorithms achieved a minimum accuracy of 68% and a maximum accuracy of 84%. By establishing this benchmark, we set the stage for future research to utilize wildlife hospital infrastructure for larger sample collection and advanced machine learning, with the ultimate goal of developing a predictive health diagnostics tool for wildlife.