It is planned to use millimeter wave (mm-wave) communication in 5th Generation (5G) communication systems, as it allows high bandwidth and accordingly high speed data communication. Path loss is one of the most important factors affecting system performance in mm-wave communication. Therefore, path loss must be taken into account in order to create an efficient and reliable mm-wave communication system and to obtain high data rates. It is very important for 5G systems to accurately determine the propagation characteristics and path loss models of the mm-wave communication channel. Many methods have been proposed in the literature to predict path loss with high accuracy and precision in 5G systems. In this review, it is aimed to provide researchers a clear knowledge about path loss in 5G mm-wave communication systems. Papers published between 2018-2021 which based on machine learning, deep learning, neural networks and propagation measurement approach were presented, and the main results of researches related to main path loss models Close-in (CI), and Alpha, Beta, Gamma (ABG) or Floating Intercept (FI) and papers that discussed 3-D ray tracing method were summarized in clear and precise manner.