Classical energy efficiency metrics fail to predict operational performance in complex real-world systems because they implicitly assume single-stage, near-reversible conversion and obscure the cumulative effects of irreversible thermodynamic degradation. This limitation is particularly evident in aviation, unmanned aerial systems, and radar infrastructure, where improvements in engine efficiency, installed propulsion power, or transmitter output do not translate proportionally into gains in range, endurance, payload, or detection probability. Empirical evidence across fixed-wing aircraft, helicopters, drones, and radar systems consistently indicates performance saturation governed by loss propagation, thermal constraints, and finite conversion capacity rather than by energy supply. In this work, we introduce a Unified Energy Survival–Absorption–Conversion Law that reformulates useful energy and information production as a survival-limited, multi-stage process constrained by irreversible thermodynamics and internal reaction–transport limits. We define an energy survival factor, Ψ=AE/TE+ε which quantifies the fraction of absorbed energy that remains available for useful conversion after transport losses and entropy-generating dissipation. This survival factor is coupled with an internal conversion competency term derived from reaction–transport physics to yield a general performance law, Euseful=Ein⋅Ψ⋅Cint applicable across biological metabolism, mechanical propulsion, electrical power systems, aviation platforms, sensing infrastructure, and space systems. The framework is validated against independent empirical data spanning photosynthetic ecosystems, terrestrial power plants, electric vehicles, fixed-wing aircraft, helicopters, unmanned aerial vehicles, radar installations, and spacecraft subsystems. Across all domains, predicted useful output falls within observed operational envelopes without parameter fitting. In aviation, the model explains why lift generation, range, and endurance saturate despite high engine efficiencies, identifying aerodynamic and induced-flow entropy as dominant survival constraints. In drones, it accounts for rapid endurance degradation under modest payload and control overhead. In radar systems, it explains diminishing detection returns with increasing transmit power due to thermal noise, propagation losses, and entropy-limited signal processing. The proposed law is thermodynamically consistent, dimensionally closed, and experimentally falsifiable, with all variables directly measurable through standard telemetry and diagnostics. By explicitly separating energy survival from conversion capacity, this work provides a unified physical framework for diagnosing performance limits and guiding system-level optimization across transportation, aviation, sensing, and space technologies, independent of vehicle type, energy source, or operating medium. Please check the attachment for details