Quantifying disorder in composite microstructures is essential for predicting me chanical, thermal, and electrical properties, yet traditional order parameters require prior knowledge of reference structures and fail in high-noise regimes. We present a computational framework based on delay-coordinates dynamic mode decompo sition (DC-DMD) and Shannon entropy that provides a universal, parameter-free disorder metric. The method transforms a two-dimensional microstructure into a one-dimensional scan via a serpentine space-filling curve, embeds the scan into de lay coordinates, computes the DMD eigenvalue spectrum, and evaluates the Shan non entropy of normalized eigenvalue powers. On synthetic composites (N = 500 microstructures, disorder σ ∈ [0.02,0.8], volume fractions ϕ ∈ [0.15,0.35]), geo metric entropy increases monotonically with positional disorder (Pearson r = 0.98, p < 10−6), correlates with configurational entropy (R2 = 0.96), and maintains 95% classification accuracy at signal-to-noise ratio 5 dB, outperforming bond orientational order (60%) and pair distribution function peak height (45%). The method requires no parameter tuning, computes in 2.8±0.3 seconds per 256×256 image on standard hardware, and successfully distinguishes well-dispersed, aggre gated, and percolated dispersion states in polymer nanocomposites, quantifies crys tallinity in glass-ceramics, and detects early-stage demixing in polymer blends 17 minutes before conventional peak intensity methods. This framework provides a reproducible, computationally efficient alternative to traditional order parameters for microstructure characterization, quality control, and inverse materials design.