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Tumor consistency influences meningioma handling during surgery, but systematic biomechanical classifications are lacking. In this prospective study, 129 tumor slices from 20 meningiomas underwent amplitude-sweep oscillatory rheometry (1-100% strain, 1 Hz) to characterize storage modulus (G'), loss modulus (G″), damping (tan δ), yield strain, and strain stiffening. Curves were normalized, embedded by principal component analysis, and subjected to unsupervised clustering. Three reproducible viscoelastic phenotypes were identified (Cluster A: 29%, B: 61%, C: 9%) that differed significantly across baseline stiffness, stiffening slope, yield strain, and damping (all q < 1 × 10^-7). Cluster C, defined by high stiffness and elevated dissipation, was strongly associated with intraoperative hard grading (OR 82.8, 95% CI 11.0-623.2, p < 0.0001). Tumor-level stiffness index correlated with overall consistency (ρ = 0.48, p = 0.033), and the hard-phenotype fraction tracked both maximum (ρ = 0.54, p = 0.013) and minimum consistency (ρ = 0.53, p = 0.017). Entropy-based heterogeneity did not predict surgical consistency range. Clustering robustness was confirmed by bootstrap (ARI 0.81) and leave-one-tumor-out analysis (ARI 0.79). These findings suggest a quantitative biomechanical classification of meningiomas directly linked to operative handling.