The Informational Feasibility Constraint (IFC) as a Structural Invariant of Realizable Systems This work evaluates whether the Informational Feasibility Constraint (IFC), previously identified across multiple empirical domains, satisfies the operational criteria for classification as a structural invariant governing realizable state-space trajectories in regulated systems. The analysis synthesizes prior cross-domain findings spanning cosmological structure formation, atomic and molecular systems, chemical networks, biological regulation, and engineered feedback systems. Across these physically distinct regimes, scale-dependent distinguishability exhibits bounded plateau behavior under sustained regulatory confinement. When regulation degrades, this bounded scaling collapses in structured and predictable ways. The paper distinguishes descriptive regularities from structural constraints and evaluates the IFC against established criteria for structural reality in physics, including: Observer independence Representation independence Domain persistence Counterfactual significance Constraint on realizability A conditional necessity argument is developed showing that any system exhibiting sustained regulated structure, persistent distinguishability, and time-ordered evolution must impose a feasibility constraint on admissible state trajectories. The resulting structural declaration does not assert ontological primitivity or metaphysical fundamentality; rather, it establishes that the IFC functions as a representation-independent constraint on realizable dynamics within regulated systems. Explicit empirical and theoretical falsification conditions are provided, along with a minimal formal state-space formulation clarifying the bounded scaling (plateau) condition that defines the constraint. This work reframes regulated structure not merely as an outcome of dynamical laws, but as evolution within feasibility-bounded state manifolds. The results provide a structural foundation for further formal derivation, emergence analysis, and mathematical closure of the Informational Feasibility Constraint.