# PRISMA on Machine Learning Techniques in Smart City Development
###### Abstract  
This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately, this work uniquely examines their intersection, highlighting the transformative potential of ML in urban development. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic and reproducible approach was employed to review 42 relevant studies. The analysis reveals four key themes: transportation and traffic optimization, people and event flow tracking, sustainability applications, and security use cases. These findings underscore ML's ability to revolutionize smart city initiatives by enhancing efficiency, sustainability, and security. This review identifies significant research gaps and proposes future directions, positioning ML as a cornerstone in the evolution of intelligent urban environments.  
sustainable development; machine learning; smart cities; smart energy; technological changes; PRISMA; optimization and control techniques +
Footnote †: journal: Medical  
and deep learning models, have shown success. These algorithms effectively foresee traffic patterns and recommend dynamic route adjustments to alleviate congestion [9].  
The application of machine learning techniques also allows smart cities to consume energy more efficiently and manage it more effectively. Predictive models based on machine learning are used to optimize energy distribution, demand forecasting, and resource allocation. Machine learning algorithms are used to adapt to changing energy demands as they arise in energy-efficient buildings and smart grids [10; 11].  
Through predictive policing, video analytics, and crime pattern recognition, the literature shows that machine learning is emphasized as an aid to improving public safety. Law enforcement agencies can allocate resources effectively based on ML models, which analyze historical crime data to predict hotspots [12; 13].  
Due to the detrimental effects of air pollution, it is vital to have a reliable model for predicting Air Quality Index (AQI) levels to improve urban public health and foster sustained social progress. In smart urban settings, machine learning applications in health encompass the prognoses of illnesses, monitoring patients, and optimizing healthcare resources. Predictive analytics and machine learning frameworks support prompt identification of disease outbreaks, customized treatment strategies, and effective delivery of healthcare services [14; 15].  
Furthermore, the integration of the Internet of Things (IoT) data with advanced ML algorithms is being explored to optimize various urban functions, such as water and energy management. These algorithms are designed to adjust consumption patterns dynamically, considering user convenience and sustainability goals. Given the diverse parameters and variables inherent in each system, the primary challenges lie in achieving scalability and adaptability to diverse urban parameters, which necessitates precise control and simulation methodologies [16; 17]. Ultimately, the system analysis and design will incorporate more realistic user consumption profiles.  
By developing smart city solutions, machine learning techniques have demonstrated substantial potential to address the challenges modern cities face. Researchers and practitioners will continue to explore innovative applications of machine learning in diverse fields as technology continues to advance, contributing to the development of smarter, more efficient, and sustainable urban environments.