The development of functional thin films is essential for advancing applications in energy storage, catalysis, electronics, and optics. Synchrotron X-ray microscopy offers spatially resolved, elementally sensitive mapping of materials structure at the nanoscale. Nonetheless, traditional data analysis methods struggle to process the high-dimensional datasets efficiently and precisely, often leading to time-intensive, difficult analysis. This dissertation presents several data-driven approaches for synchrotron-based scanning X-ray diffraction microscopy characterization of functional thin films, incorporating advanced data processing techniques such as data science and machine learning to enhance data interpretation and optimize analysis time. By integrating unsupervised clustering, deep learning, and physics-aware automatic differentiation, the proposed methodologies enable rapid analysis of thin film structural morphology, which plays a critical role in fundamental materials properties. We demonstrate the utility of these approaches through case studies on materials relevant to electrocatalysis and microelectronics, showcasing improvements in both the accuracy and speed of feature extraction, as well as explore potential applications toward in situ and operando experiments. This work not only establishes a robust framework for the data-driven analysis of X-ray diffraction microscopy data but also provides insights into the structure-property relationships key to improving the performance of functional thin films.