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The mutual acceleration of the development of science and technology has radically transformed almost every corner of our lives in the recent decades. The scientific method has also undergone a paradigm shift in numerous areas, which was induced by the multifold increase in the amount of measured data and computing capacity, as well as the development of data storage and data processing methods. It is indisputable that during these years, computer simulations and machine learning methods have revolutionized countless areas of the industry and science in the past years and are still trying to conquer new fields with yet seemingly unstoppable momentum. Research in astronomy and astrophysics has a particularly long history of using these modern solutions. Where various sky survey projects have been producing huge amounts of data for decades, the handling of which has always required the latest tools. In parallel, the focus of cosmological research slowly turned towards our currently accepted cosmological standard model, the $\Lambda$CDM. Technical development inherently increased the accuracy of our measuring instruments, which brought light to the countless contradictions and inaccuracies in the $\Lambda$CDM. Efficient data collection of surveys investigating important cosmological questions, as well as analyzing them as quickly and accurately as possible has thus become the main focus of interest in modern astronomy. We are currently witnessing the transition in which computer simulations, as well as machine learning-aided data processing of measurements are heavily reshaping our knowledge about cosmology and the universe in which we all live.