Abstract: This paper introduces a universal model for understanding transformation in systems where the rules of change are themselves subject to change. It proposes a framework in which the driving force behind evolution, adaptation, or learning is not fixed, but recursively self-modifying. Rather than relying on predetermined laws or linear dynamics, this approach treats the intensity of transformation as a dynamic, evolving factor influenced by the systems own history, current state, and internal logic. By embedding recursion into the mechanism of change itself, this model allows for systems to not only respond to external inputs but to recalibrate how they interpret and apply change over time. It is especially suited to ambiguous, abstract, or emergent domains such as the evolution of ideas, the development of intelligence, cultural shifts, symbolic transformations, and metaphysical growth. The model functions like a living feedback loop: the more a system changes, the more it learns how to change, creating either stabilization, collapse, or exponential acceleration depending on recursive conditions. This recursive engine of evolution invites broad application from adaptive technologies and cognitive modeling to speculative philosophies of transformation and offers a bridge between quantitative logic and qualitative emergence. In doing so, it marks a shift toward viewing change not just as a process, but as a recursive force capable of redefining its own nature. It is a model not only of what evolves, but how evolution itself evolves.