In the present work, we introduce a machine learning-based approach for galaxy clustering. It requires to determine clusters to provide further galaxies groups’ masses estimation. The knowledge of mass distribution is crucial in dark matter research and study of the large-scale structure of the Universe. State-of-the-art telescopes allow various spectroscopy range data accumulation that highlights the need for algorithms with a substantial generalization property. The data we deal with is a combination of more than twenty different catalogues. It is required to provide clustering of all combined galaxies. We produce a regression on the redshifts with the coefficient of determination R 2 equals 0.99992 on the validation dataset with training dataset for 3,154,894 of galaxies (0.0016 < z < 7.0519). The application of a modern hierarchical density clustering algorithm for the separation of the groups of the galaxies allows us to obtain a result that is consistent with the work [4].