# PRISMA on Machine Learning Techniques in Smart City Development
## 3 Results  
In the dynamic landscape of urban development, the emergence of smart cities has become a solution to address the challenges posed by rapid urbanization, environmental sustainability, and resource management. A comprehensive review utilizing the PRISMA integrative literature approach sheds light on the interplay between machine learning techniques and various aspects of smart city infrastructure optimization. Through the analysis of the 42 studies that resulted at the end of the PRISMA chart, four major themes have emerged, each contributing to the overarching goal of creating eco-friendly and efficient urban environments.  
(1) Transport Systems and Traffic Flow (13 papers): Efficient transport systems and traffic flow are crucial for alleviating traffic congestion, improving air quality, and boosting the overall quality of life in urban areas. By optimizing transportation, cities can minimize travel times, reduce fuel consumption, and decrease greenhouse gas emissions. Numerous studies highlighted the application of machine learning in predicting traffic patterns, managing transportation networks, and improving road safety. These studies demonstrate the potential of smart technologies to transform urban mobility.  
(2) Flow Tracking System (13 papers): Flow tracking systems are essential for monitoring and managing the movement of people and goods within a city. These systems help in ensuring the efficient use of infrastructure and resource allocation, enhancing public safety, and supporting effective urban planning. This theme was identified due to the significant focus on the use of machine learning to analyze and predict human flow patterns, event dynamics, and other movement-related aspects in urban environments. These insights are vital for optimizing city operations and improving the quality of urban life.  
(3) Green Innovation (Energy) in Smart Cities (12 papers): Sustainable energy solutions are a cornerstone of smart city initiatives. Green innovation in energy management helps in reducing the environmental footprint, promoting energy efficiency, and supporting the transition to renewable energy sources. The PRISMA analysis revealed this theme as a major area of research due to the numerous studies on smart street lighting, energy waste management, and air and water quality monitoring. These studies underscore the role of machine learning in advancing sustainability and environmental stewardship in urban settings.  
(4) Security/Cybersecurity (4 papers): Ensuring the security of urban infrastructures and protecting them from cyber threats is fundamental for the resilience and reliability of smart cities. Robust security measures are needed to safeguard sensitive data, maintain public safety, and prevent disruptions. This theme emerged because of the growing concern over cybersecurity threats in increasingly connected urban environments. The literature highlighted the application of machine learning in detecting and mitigating security breaches, thus ensuring the safe operation of smart city systems.  
These four themes emerged from the PRISMA analysis as they represent the most critical and frequently addressed areas in the literature on machine learning applications in smart cities. The comprehensive review highlighted these themes as pivotal to achieving the overarching goals of efficiency, sustainability, and resilience in urban environments. Each theme addresses specific challenges faced by cities and showcases the transformative potential of machine learning technologies in creating smarter, more sustainable urban spaces.