Numerous physical, chemical, and biological factors influence coral resilience in situ, yet current models aimed at forecasting coral health in response to climate change and other stressors tend to focus on temperature and coral abundance alone. To develop more robust predictions of reef coral resilience to environmental change, we trained an artificial intelligence (AI) with seawater quality, benthic survey, and molecular biomarker data from the model coral Pocillopora acuta obtained during a research expedition to the Solomon Islands. This machine-learning (ML) approach resulted in neural network models with the capacity to robustly predict (R² = ~0.85) a benchmark for coral stress susceptibility, the “coral health index,” from significantly cheaper, easier-to-measure environmental and ecological features alone. A GUI derived from an ML desirability analysis was established to expedite the search for other climate-resilient pocilloporids within this Coral Triangle nation, and the AI specifically predicts that resilient pocilloporids are likely to be found on deeper fringing fore reefs in the eastern, more sparsely populated region of this under-studied nation. Although small in geographic expanse, we nevertheless hope to promote this first attempt at building AI-driven predictive models of coral health that accommodate not only temperature and coral abundance, but also physiological data from the corals themselves.