The study evaluated the anatomy of the nonvascular prepontine cistern affecting trigeminal nerve vulnerability in 60 patients with trigeminal neuralgia (TN) and 60 healthy controls using MRI and machine learning. Patients with TN had thinner nerves (especially in the pore), larger areas of Meckel's cave (axial and coronal planes and height), smaller sagittal angles, and shorter cisternal length; the difference in pore diameters, Meckel's cave areas (Holm's p < 0.01) and sagittal angle (Holm's p = 0.0092) remained significant. The two neuroradiologists achieved 97% agreement in the assessment of neurovascular conflict (κ = 0.91). Machine learning models (SVM, Random Forest, XGBoost and others) achieved high discrimination ability: SVM had a PR-AUC of 86.16 ± 4.39% and a ROC-AUC of 87.40 ± 4.52%. SHAP and LIME identified pore-level diameters and Meckel's cave measurements as major contributors. Nonvascular variations in the prepontine cistern, particularly pore size, cavernosum, and sagittal angle, contribute to the pathophysiology of TN. AI-assisted morphometry provides reproducible discrimination for integration into diagnosis and treatment.