Clusion of experimental and non-experimental study to completely have an understanding of the phenomenon of concern [58]. Additionally, it makes it possible for for combining evidence from the theoretical and empirical literature. A related form of overview was carried out by Hao et al. [36]; on the other hand, it was restricted only to Chinese studies and concerned only the use of massive data, although this study focuses on the worldwide use of AI-based tools for major information analytics. This integrative systematic literature critique was depending on the following steps presented by Whittemore and Knafl [59]: (1) identification in the issue, (two) literature search, (three) information evaluation, (4) information evaluation, and (five) presentation, even though the methodology was adjusted towards the unique field of study. Identification from the issue was determined by seeking an answer to the investigation questions that have been formulated in the introduction. For literature analysis, the author MCC950 Technical Information analysed research papers around the application of major information analytics and AI-based tools in urban planning and design and style. The included papers have been sourced from the Web of Science Core Collection using the key phrases `ARTIFICIAL INTELLIGENCE’ and `URBAN/CITY/CITIES’ to construct the initial corpus of literature. These keyword phrases were sought in the titles, the key phrases with the papers, along with the abstracts. The second literature query was conducted utilizing the terms `BIG DATA’ and `URBAN/CITY/CITIES’ as keyword phrases; as a result, PF-06873600 Biological Activity because it integrated numerous unrelated searches, though by far the most significant sources seem on each on the abovementioned searches, the latter search was abundant. Books and book chapters have been excluded in the query. Following this search, only papers in the urban research, regional urban arranging, geography, architecture, transportation, and environmental research categories have been incorporated. The resulting database that consists of 134 papers was imported into the Mendeleysoftware. Additional, 54 papers in the seed corpus not fitting the scope were manually removed, e.g., like research of the use of AI in building or innovation policy evaluations. This evaluation of the abstracts narrowed the study to 82 papers. In the information evaluation phase, this core literature was analysed from various perspectives. Because of the diverse representation of principal sources, they were coded based on a variety of criteria relevant to this review: year of publication, study centre, type of paper (theoretical, assessment, and experimental), variety of data, and AI-based tools that have been made use of. This permitted for the identification of publications related to, among other people, the most renowned data centres including Media Lab MIT, Senseable City Lab MIT, Centre for Advanced Spatial Evaluation UCL, Future Cities Laboratory, and Urban Significant Information Centre. The final sample for this integrative evaluation integrated empirical research (64), theoretical papers (4), and evaluations (14). Only 9.7 from the papers had been published before 2010. The primary sorts of information utilised are mobile phone data, volunteered geographic information data (including social media information), search engine information, point of interest information, GPS data, sensor data, e.g., urban sensors, drones, and satellites, information from both governmental and civic equipment, and new sources of big volume governmental data. Data analysis began using the identification of opportunities and barriers to foster or prevent the use of large data and AI in emerging urban practices. Strengths and limitations from the use of diverse types of urban large information analytics depending on AI-based tools were identi.