Galaxy morphology is a fundamental tracer of galaxy assembly, but its characterization becomes increasingly uncertain in the early Universe. We investigate morphological classifications across different methodologies over the redshift range , using JWST observations of 15,567 galaxies with 6,099 synthetic galaxies from IllustrisTNG. To quantify morphology without relying on visual labels or prescribed light profiles, we use an unsupervised-supervised hybrid machine-learning approach based on the non-parametric MEGG system, which combines the second moment of the light distribution, Entropy, Gini, and Gradient Pattern Analysis. We find that parametric and non-parametric classifications remain broadly consistent at , but diverge systematically at higher redshift. We identify this epoch as the Morphological Indeterminacy Regime (MIR), in which classical disc and spheroidal categories no longer provide a unique description of the galaxy population. The fraction of mixed-interpretation galaxies increases with redshift, reaching at . Cross-domain and projection-invariance tests show that the classifications are stable and insensitive to data set and viewing angle. In the interval , MIR galaxies occupy an intermediate stellar-mass regime (median; ), between low-mass discs (median; ) and massive spheroids (median; ), while irregular systems have stellar-mass distributions similar to those of MIR galaxies. We interpret these results as evidence that morphological indeterminacy is a characteristic feature of galaxy assembly in the early Universe, reflecting a population of dynamically young systems that have not yet settled into the classical Hubble sequence.

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