The AI/ML techniques have been successfully used for both forward (structure to property) and inverse (property to structure) tasks in materials design. Generating crystal structures from XRD can be considered a generative AI-based inverse design task. Recent advancements in machine learning related to X-ray diffraction include works by Park et al., NeuralXRD, XRD is All You Need, Crystallography Companion Agent (XCA), ARiXD-ML, Zaloga et al., XTEC, Li et al., Maffettone et al., Oviedo et al. and several others. These works demonstrate the application of ML models for a wide range of tasks, including crystal lattice and space group classification, peak detection, and structure generation. In particular, the application of deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) has demonstrated the ability to generate complex atomic structures based on insights.