Galaxies have complex formations of components such as stars, dust, and gas, whose spatial and temporal relationships can help us to better understand the formation and evolution of galaxies, and ultimately the Universe. The main objective of this study is to test how machine learning can be used to classify galaxy components and stellar ages within spiral galaxies based on values of pixels in Hubble Space Telescope imagery, Euclidean distance calculations, textural features, and band ratios. We develop two machine learning models using maximum likelihood, random forest, and support vector machine algorithms. We find the models are successful for classification of galaxy components and stellar age, with Euclidean distance and textural features being the most important parameters. These methods can contribute to the rapid processing of high resolution astronomical imagery of galaxies and other celestial phenomena
