Stone disease management is continuously evolving through the introduction of novel tools and technologies. Artificial intelligence and machine learning (ML) promise a new technological frontier for the enhancement of urolithiasis diagnosis, treatment, and prevention. This article focuses on the potential for ML algorithms to improve urolithiasis-directed imaging and enhance outcome prediction for spontaneous stone passage, ureteroscopy, shockwave lithotripsy, and percutaneous nephrolithotomy. We also discuss how ML optimizes stone composition evaluation and urinary abnormality detection. Ultimately, we aim to shed light on how ML-based innovations will help personalize treatment and improve the efficiency of stone disease management.