The structures of many material systems evolve as they are treated with physical processing. For instance, organic and inorganic crystalline materials frequently coarsen over time as they are thermally treated; with domains (grains) rotating and growing in size. When a material system undergoing the structural transformation is probed using x-ray scattering beams, the peaks in the scattering images will sharpen and intensify, and the scattering rings will become increasingly’textured’. Accurate identification of the transition frame in advance brings multiple benefits to the NSLS-II in-operando experiments of studying material systems such as minimal beamline damage to samples, reduced energy costs, and the optimal sampling of material properties. In this paper, we formulate the prediction and identification of the structural transition event as a classification problem and apply a novel LSTM model to identify sequences having transition event. The preliminary results of the experiments are encouraging and confirm the viability of the detection and prediction of transition in advance. Our ultimate goal is to deploy such a prediction system in the real-world environment at the selected beamline of NSLS-II for improving the efficiency of the experimental facility.