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Extrapolation in disordered materials such as glasses, amorphous solids, and liquids is fundamentally harder than in crystalline systems. Disordered materials lack periodic symmetry, exhibit highly heterogeneous local environments, and possess variable system sizes that range from hundreds to hundreds of thousands of atoms. These characteristics create an exceptionally large hypothesis space for machine learning models and render pure statistical learning approaches unreliable beyond the training distribution. At the same time, traditional physics-based models remain too approximate for quantitative accuracy in complex disordered systems. This conceptual framework proposes a unified model that bridges statistical learning and physics priors to overcome these limitations and enable reliable extrapolation in disordered materials. The framework rests on three core physics priors—locality, smoothness, and invariance—that act as powerful inductive biases. Locality limits interactions to finite cutoffs, smoothness ensures continuous property landscapes, and invariance (rotational, permutation, and size extensivity) dramatically reduces the effective search space. These priors are embedded directly into statistical learning architectures so that the overall prediction combines a physically grounded baseline with data-driven residual corrections. In extrapolation regimes the physics-informed baseline dominates, producing graceful degradation rather than arbitrary outputs. The model further integrates statistical learning components including uncertainty quantification to flag risky predictions, active learning to expand the training distribution adaptively, multi-fidelity strategies that leverage cheap physics approximations, representation learning for cross-system transfer, and ensemble methods for robustness. The resulting conceptual taxonomy clarifies why extrapolation fails in disordered materials and supplies explicit design principles for extrapolation-aware models. This unified approach shifts the default paradigm in computational materials engineering from purely data-driven or purely physics-driven methods toward a hybrid that respects physical constraints while retaining the flexibility of statistical learning. The framework is expected to accelerate discovery in glass design, amorphous polymers, and liquid electrolytes where out-of-distribution generalization is essential. By treating physics priors as non-optional architectural elements rather than optional regularizers, the model offers a practical path toward trustworthy machine learning predictions in the disordered realm.