This paper introduces the Morph Model, a mathematical framework for adaptive function learning based on direct functional updates rather than iterative gradient descent. A morph is a finite, scoped functional transformation f -> f + delta derived from finite-difference directional estimates and interpolation along promising update directions. Under standard smoothness, exploration, and noise assumptions, we prove convergence to an O(epsilon^2) stationary regime and obtain linear rates under strong convexity. We further show how morphs propagate through composite architectures including weighted ensembles and mixture-of-experts, providing a training-optional alternative to traditional optimization. This framework offers a mathematically grounded and computationally efficient update mechanism suitable for real-time adaptive systems.