Data-driven methods including machine learning are widely used in materials properties prediction and devices design due to their abilities to discover the underlying statistical correlations from data, achieve target prediction efficiently and accurately, and assist in the analysis of physical images behind the data. In recent years, machine learning modeling in the research of dielectric ceramics and devices becomes popular. Recent development on the research of machine learning prediction models for key properties (i.e., dielectric constant and quality factor of microwave dielectric ceramics) was represented. Machine learning methods in dimensional optimization, failure analysis, etc. for antennas and filters were also introduced. In addition, some prospects of data-driven studies in materials and devices were also provided.