# PRISMA on Machine Learning Techniques in Smart City Development
## 4 Limitations  
One limitation of this study pertains to the exclusion of articles published in languages other than English, acknowledging the strength of academic research in Chinese, French, Japanese, German, etc. This exclusion could be a notable constraint. Another limitation involves excluding non-academic publications, recognizing that despite lacking rigorous blind peer review, commercial research can offer practical and timely insights. Third, the review's reliance on published articles means that unpublished works, such as conference presentations, technical reports, or dissertations, may not have been included. These sources can sometimes provide valuable insights and preliminary findings that precede formal publication. Consequently, the exclusion of such gray literature might result in a less comprehensive understanding of the current research landscape. Another limitation is influenced by the evolving nature of the field of machine learning in smart city development, which is relatively young and lacks established institutions and research patterns, facing challenges such as an inadequate vocabulary and research frameworks. The field suffers from an inadequate vocabulary and standardized terminologies. As machine learning applications in smart cities are interdisciplinary, involving elements from computer science, urban planning, engineering, and environmental sciences, there is often confusion and miscommunication among researchers and practitioners due to the lack of a unified language. This fragmentation hampers effective collaboration and slows down the progress of research and implementation. Also, the research frameworks and methodologies in this domain are still underdeveloped. There is no agreement on the optimal practices for integrating machine learning with urban systems, which leads to inconsistencies in study designs and evaluation metrics. This inconsistency makes it difficult to compare results across different studies, hindering the ability to draw generalizable conclusions and develop comprehensive models. The rapid pace of technological advancement in machine learning means that research findings can quickly become outdated. This constant state of flux requires continuous updating of knowledge and skills, which can be resource-intensive for researchers and practitioners. Additionally, the integration of cutting-edge technologies into existing urban infrastructure poses practical challenges, including compatibility issues and the need for substantial financial investments. Moreover, the field's relative youth means that there is a scarcity of longitudinal studies that can provide insights into the long-term impacts and sustainability of machine learning applications in smart cities. Most current studies focus on short-term outcomes, leaving a gap in understanding how these technologies will perform and evolve over time. The fourth limitation is associated with the constraints imposed by the selected articles in this review. We recognize that our search parameters might have overlooked significant articles, and it is possible that relevant studies were published after our search. The selection criteria, such as specific keywords, databases, and time frames, inherently restrict the scope of the review. While we employed a comprehensive and systematic approach using the PRISMA methodology to identify relevant studies, there is always a possibility that some pertinent research was excluded. This exclusion could be due to the use of different terminologies, variations in indexing across databases, or publication in less accessible journals. Despite these constraints, we have endeavored to present a thorough and balanced overview of the application of machine learning techniques in smart city development. Our analysis highlights critical themes and discoveries, offering a detailed examination of the existing literature. This comprehensive review provides important perspectives on the present state of research, identifying gaps and suggesting future research directions.