Despite 2 decades of effort, there is a lack of clinically deployed models for predicting incident, severe, or recurrent Clostridioides difficile infection (CDI). This review outlines the promise of machine learning and biomarker-augmented models for targeted prevention and treatment, but also emphasizes the challenges of real-world deployment—namely integration into clinical workflows and governance. Moving forward, progress will depend on translational biomarker development, pragmatic modeling pipelines, and continuous monitoring. With these elements in place, CDI prediction tools can become a template for precision prevention of healthcare-associated infections.