## 2 Methodology  
We found articles about 5G and 6G by using three keywords: \"machine learning\", \"internet of things\", and \"energy\". The searches were conducted using three digital libraries: (1) Web of Science (WOS), which provides multidisciplinary research articles in the fields of science, arts, and technology; (2) IEEE Explore, which provides articles specialised in the field of electrical and electronics engineering; and (3) ACM digital library, which has a comprehensive database of scientific articles related to computing and technology. The significant articles and literature referred to by the search outputs were selected and categorised based on two criteria: (1) use three iterations in the filtering process, which removes the redundant and duplicated articles, excluding irrelevant articles using the title; and (2) use three iterations in the categorization process, which categorises the significant articles and literature referred by the search outputs. Perform the initial screening, and the selected papers are then subjected to a second selected using the 5G and 6G after carefully reviewing the results of the reduced search. The three databases mentioned were searched extensively in May 2021, with many different keywords (or phrases) being used, including \"5 generations\" or \"5G\" or \"6 generations\" or \"6G\" and \"machine learning\" or ML or \"artificial intelligence\" or \"AI\" or \"classification\" or \"clustering\" or \"regression\", and \"IoT\" or \"Internet of Things\" and \"Energy\" or \"techniques\" or \"methods\" or \"implementation\" or \"guide\". Figure 2 depicts an example of a query text that might be utilised. Using the advanced features of search engines, we have removed search results that are correspondences, letters, book chapters, and other types of documents. The exclusions are designed to ensure that only the most recent scientific publications are obtained, as well as just those of significant value that improve the 5G and 6G capabilities. The emphasis is on including any articles and scientific submissions that meet all of the requirements for inclusion in this work. Following that, they are separated into two categories, namely, general and coarse-grained classifications. Following the study's findings, the latter is explored in four succeeding  
Figure 2: Diagramatic view of the organization of this survey paper. sections derived from the results, in which the Google scholar search engine was applied to determine the path of the study. After the queries were run, 1574 papers were found, with 459 coming from WOS, 578 from IEEE, and 537 from ACM digital libraries. Between 2014 and 2021, all of the articles on this list were published. These articles were then sorted into three groups: (1) 460 redundant articles, (2) 744 irrelevant articles based on titles and abstracts, and 370 articles that met the requirements for 5G, 6G, machine learning, internet of things, and energy. As previously stated, an article is removed from consideration if it fails to meet the following selection criteria: (1) The paper was not written in an English-speaking environment. (2) The article's emphasis was on techniques and/or procedures. (3) The article's research focus is solely on 5G and 6G, with no mention of the internet of things, energy, or machine learning. Furthermore, if 5G and 6G were not added in the second iteration, the articles would still be removed. (1) There are no features of machine learning, IoT, or energy considered in the paper's contribution. (2) The paper's discussion is limited to 5G and 6G, with no other topics addressed. Articles are subjected to rigorous machine learning, internet of things, and energy analysis in this study, with the remaining articles being sorted into categories that focus on how to improve 5G and 6G. Results and Statistical Information of Articles  
The review's findings are addressed in the form of responses to the research questions. The taxonomy is shown in Figure 3. There are four basic categories in which the recordings might be classified.