A machine learning model successfully predicted which individuals would maintain weight loss 12 weeks after a 5-day fast, using baseline gut microbiome composition and clinical markers. The study tracked 38 healthy participants through fasting and follow-up, identifying specific bacterial species—including an unclassified Faecalibacterium and Oscillibacter sp.—alongside LDL cholesterol and systolic blood pressure as key predictors. The model validated across three independent cohorts including metabolic syndrome patients and multiple sclerosis patients. This represents a significant advance in personalized nutrition, suggesting that individual microbiome profiles could guide fasting recommendations rather than the current one-size-fits-all approach. The finding that baseline microbiome diversity correlates with post-fasting changes aligns with emerging research on gut bacteria as metabolic regulators. However, the small initial cohort and focus on healthy adults limits broader applicability. The validation across disease populations is encouraging, though larger diverse samples are needed. As a preprint awaiting peer review, these promising results require confirmation before clinical application. If validated, this could enable precision fasting protocols tailored to individual microbiome signatures, potentially improving outcomes for metabolic health interventions.