# PRISMA on Machine Learning Techniques in Smart City Development
## 2 Methodology  
To align with the research questions and identify key search terms, this study employed the PRISMA 2020 flow diagram methodology. An acronym for Preferred Reporting Items of Systematic Reviews and Meta-Analyses, PRISMA is a set of guidelines for conducting systematic literature reviews and meta-analyses. Research articles conducting literature reviews and meta-analyses in the past did not follow stringent scientific reporting guidelines, resulting in inaccurate evaluations. The Quality of Reporting of Meta-analyses (QUOROM) was formed in 1999 to address this concern, focusing on the reporting of meta-analyses specifically conducted in randomized controlled trials [18]. The statement was updated in 2009 and renamed PRISMA to reflect advancements in systematic review methods [19]. When examining an extensive publication collection, including peer-reviewed academic articles, this approach ensures a transparent, thorough, and precise presentation [20]. This study adhered to PRISMA 2020 guidelines to ensure accurate and comprehensive reporting. Consequently, the aim is to furnish readers with a comprehensive, reliable, and lucid comprehension of both the research techniques and the subject matter. The methodology employed in this study follows the PRISMA guidelines, which are widely recognized for ensuring a systematic and transparent approach to reviewing literature. The PRISMA methodology includes the following key steps:  
* Database Search: The research was conducted across multiple academic databases. * Inclusion and Exclusion Criteria (articles were selected according to their relevance of the intersection between machine learning and smart cities). Screening Process (automated removal of duplicates and irrelevant entries, manual screening). * Final Selection: A detailed examination of the remaining articles, applying stringent exclusion criteria. As we conducted the final stages of the research, where exclusion was of the utmost importance, we consistently addressed these questions: \"Is the study relevant to the paper's purpose?\" and \"Is the study acceptable for the paper?\" [21]. These questions were addressed first to the criteria the authors developed in the final stages of the research to identify the main exclusion criteria. Subsequently, we applied the same queries to the paper under review, integrating the exclusion criteria that we had established. Additionally, we focused on the main aspects that should be present in a criterion when validating the exclusion criteria [21]: \"(a) study population, (b) nature of the intervention, (c) outcome variables, (d) time period, (e) cultural and linguistic range, and (f) methodological quality\" [21]. To ensure that a good number of papers are included in Section 3, papers that did not meet the exclusion criteria were removed. This process was supplemented by principles from the Cochrane Handbook for Systematic Reviews of Interventions, which similarly emphasizes rigorous criteria for inclusion and exclusion, ensuring the relevance and quality of studies selected for review. The integration of both PRISMA and Cochrane methodologies underscores the importance of a meticulous and replicable approach to systematic reviews [22]. To have a comprehensive and integrative literature review on machine learning techniques in smart city development, in January 2024, the research was conducted on academic articles published in Scopus, SSRN, Mendeley, Emerald, Springer LINK, Science Direct, ERIC, MDPI, JSTOR, IEEE Xplore Digital Library, ACM Digital Library, and Google Scholar (first 10 pages). These databases are the most recognized and widely used in academia, containing extensive information on the topic. Articles chosen for review were required to mention \"machine learning\" and \"smart cities\" in their title, abstract, or research focus. Excluded from the study were books, conference papers, commercial reports, opinion pieces, and editorials.