Here, we focus on the applications of ML in solid-state X-ray structural data visualization and structure prediction, providing researchers with a comprehensive overview of the current stateof-the-art and challenges in harnessing ML for structural analysis. Importantly, we hope to bridge the conceptual divide between known physics and purely data-driven ML to promote understanding and sensible implementation of ML methods. We aim to supplement existing reviews on the application of ML in photovoltaic device design , semiconductor property design , microstructure design , materials property design , device engineering , and the broader field of materials science , focusing primarily on the representation and prediction of structural X-ray data. Finally, we discuss best practices in data curation, The summarized approaches should enable more robust analysis in high-throughput studies and increase confidence in adopting ML analysis in closed-loop experimental protocols.