This article reviews the evolution of X-ray diffraction (XRD) analysis, from traditional methods to modern machine learning approaches. Traditional techniques, such as Rietveld refinement and Search/Match Libraries, are compared with four emerging ML architectures: Convolutional Neural Networks (CNN), Transformer Encoders, CNN-MLP hybrids, and Variational Autoencoders (VAE). Each of these approaches offers distinct advantages for addressing contemporary challenges in XRD analysis, including handling peak complexity in multi-phase samples, processing large volumes of data, managing experimental artifacts, and facilitating dynamic analysis. A comparative evaluation of these methods is conducted based on processing speed, multi-phase capability, interpretability, and scalability.