Here, we build on these advances and report a machine learning method that rapidly, automatically, and accurately determines the morphology of block copolymers SAXS data to enable real-time data analysis. Unlike conventional machine-learning methods for analyzing SAXS data that process the full intensity curve without explicit noise filtering, our approach is informed by X-ray scattering theory and automatically extracts pivotal physics-informed morphological features from the reduced 1D intensity scattering pattern to construct a universal classification model and enable real-time data analysis for researchers. Statistically, our method introduces two major innovations. First, by modeling each intensity curve with a Gaussian process, we incorporate experimental uncertainties across different wave vector magnitudes, q. Second, by integrating the physical principles of X-ray diffraction, we enhanced the effectiveness and accuracy of our predictive models. X-ray diffraction measures the power spectrum of the Fourier transform of the sample's electron density. Thus, details of a sample's phase and nanostructure manifest most prominently as diffraction peaks in the X-ray data profile; where the q-location of the strongest peak, peak width, and peak locations of all peaks with respect to the primary peak are the most important features with direct physical significance. With this knowledge, full-intensity curves were instead trans-formed into a small set of informative features, dramatically reducing the required size of the training set.