Background/Objectives: Predicting kidney stone recurrence after active stone treatment remains challenging due to its multifactorial nature. Artificial intelligence, particularly machine learning, provides new methods for identifying hidden patterns in high-dimensional clinical data. We conducted a study applying machine learning to identify key predictors of recurrence following endoscopic combined intrarenal surgery (ECIRS) in patients with calcium stones. Methods: This retrospective cohort analysis included 72 patients with calcium stones who underwent ECIRS between June 2019 and May 2022 and achieved a complete stone-free status on postoperative CT. Patients were followed for two years, with recurrence assessed through protocolized imaging. We collected 235 variables, including clinical data, 24 h urine collections, stone composition, imaging features, and perioperative findings. Several machine learning models were developed, and SHapley Additive exPlanations (SHAP) analysis identified features associated with recurrence. Results: Within two years, 29 of 72 patients (40.3%) experienced recurrence. The TabNet model demonstrated the highest predictive accuracy (AUC = 0.89), outperforming traditional machine learning algorithms. SHAP analysis identified urinary oxalate ≥ 25.4 mg/day and hemoglobin (Hb) drop ≥ 0.3 g/dL at 3 months postoperatively as independent predictors, even within normal limits. A simplified TabNet-based model using three key features (oxalate, urine volume, and 3-month ΔHb) maintained a strong performance (AUC = 0.75), supporting its clinical utility. Conclusions: Machine learning enabled the accurate prediction of kidney stone recurrence after ECIRS. The inclusion of 24 h urine data significantly improved the performance. Even patients with “normal” oxalate levels showed increased risk, suggesting current clinical thresholds may require re-evaluation.