# PRISMA on Machine Learning Techniques in Smart City Development
## 5 Conclusions and Discussions  
The fragmented nature of current research on machine learning (ML) applications within smart cities necessitates an integrative approach to fully understand their transformative potential across diverse urban domains. Existing studies often focus narrowly on specific subareas such as transportation optimization, energy management, or security applications. While these focused studies contribute valuable insights, they do not offer a holistic view of how ML can revolutionize urban systems as a whole. By providing a comprehensive overview, this review bridges the gap between isolated research efforts, highlighting the interconnectedness of various urban domains. This integrative perspective is essential for creating smart cities that are technologically advanced, cohesive, and efficient at the same time. The interplay between machine learning techniques and smart city development is an emerging area of study that holds significant promise. While there is a growing body of research on ML and urban computing, few studies explicitly analyze how ML can drive smart city growth and sustainability. This review delves into this critical intersection, demonstrating how ML can enhance urban planning, infrastructure management, and service delivery. By focusing on the synergies between ML and smart city initiatives, this review uncovers new avenues for innovation and highlights the significance of adopting ML-driven strategies to tackle intricate urban issues. The findings suggest that ML is not just an add-on to existing systems but a foundational element that can redefine urban living. A critical aspect often overlooked in the discourse on ML and smart cities is the human and social dimension. Most studies emphasize technical capabilities and efficiency gains, neglecting the potential of ML to create citizen-centric intelligent urban environments. This review brings attention to the ways in which ML can enhance the quality of life for urban residents by improving public services, fostering community engagement, and ensuring equitable access to resources. By centering the discussion on citizens' needs and experiences, this review advocates for a more inclusive approach to smart city development. It emphasizes that technological advancements should not only serve operational goals but also contribute to the overall well-being of the population. Despite the technical advancements in ML applications for smart cities, there is a noticeable gap in the literature regarding practical implications for policymakers. Municipal authorities play a pivotal role in the adoption and implementation of ML technologies, yet much of the existing research fails to translate technical findings into actionable policy recommendations. At the same time, identifying the challenges and limitations associated with the integration of ML in smart cities is essential for guiding future research. This research utilizing the PRISMA methodology provides valuable insights into the current state of research on machine learning techniques and their applications in smart city development. The analysis of 42 studies reveals four major interrelated themes that demonstrate the transformative potential of machine learning across diverse urban domains. There are a few key research gaps that this article aims to address:  
1. Lack of a comprehensive overview integrating research on machine learning applications across diverse smart city domains. Many existing articles focus narrowly on specific subareas like transportation or energy without providing a big-picture view. This article provides an integrative review spanning key urban systems. 2. Scarce literature examining the interplay between machine learning techniques and smart city development. While studies on machine learning and urban computing exist, few works specifically analyze the role of ML in enabling smart city growth and sustainability. This review contributes insights situated at the intersection of these areas. 3.