Background <p>Outcome prediction after Gamma Knife radiosurgery (GKRS) for vestibular schwannoma remains largely guided by tumor size, Koos grade, baseline symptoms, cochlear dose constraints, and institutional experience rather than individualized estimates of tumor progression and functional outcomes. We developed THINKERS-VS, a mixture-of-experts (MoE) artificial intelligence framework for progression and symptom-transition prediction after GKRS.</p> Methods <p>We performed a retrospective single-center study of vestibular schwannomas treated with GKRS. Variables available before the treatment were used to train a MoE neural network with discrete-time survival modeling. The model incorporated demographic, clinical, tumor, and radiosurgical variables. The primary endpoint was tumor progression across intervals of 3, 6, 12, 18, 24, 32, 48, and 60 months. Secondary endpoints included new or worsened hearing loss, imbalance, vertigo, dizziness, and tinnitus. Internal validation used grouped 5-fold cross-validation and a grouped holdout test split. Performance was assessed using AUC and Brier score.</p> Results <p>The cohort included 686 patients. Median age was 59.5 years, median tumor volume was 0.746 cm³, median prescription dose was 12.0 Gy, and median follow-up was 52.3 months. THINKERS-VS achieved strong progression discrimination across evaluated intervals, with AUCs ranging from 0.807 to 0.859. At 60 months, AUC was 0.807 and Brier score was 0.012. For symptom-transition prediction, holdout AUCs were 0.875 for hearing loss, 0.844 for imbalance, 0.813 for vertigo, 0.884 for dizziness, and 0.839 for tinnitus.</p> Conclusions <p>THINKERS-VS provides an internally validated framework for individualized tumor progression and symptom-transition prediction after GKRS for vestibular schwannoma and recommends tumor margin dose associated with best outcome.</p> Clinical trial number <p>Not applicable.</p>