In summary, we have developed a high-throughput, material-independent workflow for characterizing the morphology of unidentified block copolymers using a physics-informed machine learning algorithm that rapidly analyzes SAXS data. Leveraging automated chromatography and controlled polymerization to generate high-quality and expansive datasets for training, our novel machine learning approach rapidly extracted 1D scattering patterns and achieved a remarkable 95% accuracy in identifying the nanoscale morphologies of diblock copolymers for new monomers not in the training data set. By integrating this automated synthesis method with advanced feature extraction and uncertainty analysis by machine learning techniques, we established a robust, high-throughput framework to enhance the predictive accuracy and efficiency of analyzing polymer phase behavior. This advancement paves the way for accelerated materials discovery and a deeper comprehension of structure-property relationships in soft materials. This research inspires several directions, including predicting a new phase rarely appearing in the training data, and inverse design to achieve a given polymer phase by optimizing chemical identity, composition, and external conditions. This approach is not limited to diblock copolymers but also holds the potential for extension to higher-order multiblock copolymers and non-linear architectures, indicating its broad appli-