Cities are complex systems with interconnected citizens, transportation, communication networks, services, businesses, and utilities aimed at improving urban lifestyles. As urban populations grow, city governments face pressure to provide essential services like housing, agriculture, and environmentally friendly buildings. Smart cities require advanced telecommunication and wireless infrastructures to effectively deliver services and connect millions of devices using Machine-to-Machine (M2M) communication, network virtualization, wireless sensor networks, and gateways [1]. Fig. 1 shows the data rate, power consumption, establishment cost, and coverage range of various communication technologies.

The Internet of Things (IoT) significantly impacts modern communication by enabling the interaction of everyday objects with microcontrollers, radio modules, and communication protocols [2], [3]. IoT is crucial for smart cities, with governments and private organizations using it in Information and Communication Technologies (ICT) to enhance service quality and reduce costs [4–6]. IoT technologies are vital for developing smart cities, managing large-scale data, and driving sustainable development. By 2030, IoT-enabled devices are expected to exceed 29 billion, highlighting IoT’s importance in creating innovative and efficient urban environments. Various strategies and technologies are being implemented to address the complexities of smart city management. IoT devices collect vast amounts of data on temperature, humidity, traffic flow, energy consumption, and air quality, enhancing city management and residents' living standards. Artificial Intelligence (AI) uses machine learning to analyze this data, identifying patterns and trends that are difficult for humans to detect [7], [8]. This is crucial for predictive maintenance, ensuring infrastructure remains in good condition. AI also supports smart water supply, energy management, waste management, and reducing congestion and pollution. Around 30% of smart city applications now integrate AI, a trend expected to grow by 2025. While AI and IoT integration offers significant benefits, it raises concerns about privacy, data security, and biases, necessitating strong ethical frameworks (Fig. 2).

The article explores the integration of IoT and AI technologies in smart cities, focusing on how these technologies enhance communication and data exchange among various city services. It emphasizes the importance of robust networking infrastructures for efficient operation. While previous reviews have covered aspects like big data and network security, this article uniquely addresses the combined impact of IoT and AI on smart cities. It discusses how IoT enables device communication, making them integral to smart city functions, and how AI can analyze the vast data from IoT sensors to improve city management and residents’ quality of life. The article is structured to introduce smart cities, explain the research methodology, and define key concepts and components.

I. IoT technologies: Discusses sensors, networks, and data analytics essential for smart cities.

II. AI algorithms: Explores AI algorithms and their potential impact on urban life.

III. Future trends: Examines trends like 5G integration and AI applications in smart cities.

IV. Conclusions: This section provides conclusions and future prospects for smart city development, emphasizing the benefits for residents, governments, and businesses.

The field of smart cities is rapidly evolving, making it ideal for an integrative literature review. To achieve this, relevant academic papers were meticulously selected and screened. Two primary databases, Google Scholar and PubMed, were used to gather peer-reviewed articles from reputable publishers, focusing on high-quality publications. The references listed in these papers identified additional resources to expand the analysis. The selection of academic papers was based on their relevance to the investigated issues, ensuring a comprehensive analysis that included both empirical and qualitative studies. Only studies demonstrating consistent findings supporting the advancement of smart city initiatives were included, while those with divergent or inconclusive results were excluded.

From 2010 onwards, PubMed was specifically used to search for keywords such as "smart city," "smart city and IoT,” and “smart city, IoT, and AI." The number of published articles was gathered and presented in Fig. 3, showing that AI, IoT, and smart cities gained significant attention after 2015, highlighting both challenges and opportunities in the smart city context.

Following the analysis of academic papers, supplementary sources like governmental reports, reputable newspaper articles, and websites were searched on Google to reinforce the conclusions. Fig. 4 provides an overview of the methodology for integrating IoT and AI applications in smart cities.

The term "smart city" has various definitions and is often used interchangeably with terms like "intelligent city" and "digital city." This ambiguity arises because there is no universally accepted definition. Smart city concepts emerged to address urbanization challenges and promote sustainable development by integrating IoT, AI, and big data analytics. These technologies aim to improve urban services, reduce resource consumption, and enhance residents’ quality of life.

Technological advancements enable modern smart cities to collect and analyze data from various applications, such as smart agriculture, healthcare, and traffic, to make informed decisions that improve living standards. The rapid development of IoT, cloud computing, and AI is crucial for enhancing urban facilities’ performance and interactivity, reducing management costs, and improving skills.

There are two main approaches to defining smart cities. The first focuses on a single urban feature, such as technology or ecology, without considering the interconnectedness of all urban components. The second approach views the smart city as a comprehensive system that integrates economic, technological, and social factors to ensure urban stability and sustainability. This holistic approach emphasizes considering all urban characteristics to address modern cities' challenges effectively.

Technological advancements could introduce millions of intelligent sensors to city infrastructures, operating with the efficiency of 1000 IoT devices. Effective data management from these sensors is crucial for the smooth operation of smart city ecosystems. Classifying applications is essential to establishing a solid foundation for smart cities and evaluating areas like governance, economy, and healthcare within the smart city context.

Smart cities rely on advanced communication technologies and IoT infrastructure, requiring specific communication systems and architecture. This infrastructure facilitates information exchange among urban stakeholders, regardless of the application. Communication is key for transmitting data between sensors and devices. Three common communication patterns are used: 1) cellular mobile networks, 2) IoT-dedicated cellular networks, and 3) multi-tier networks. Fig. 5 illustrates these architectures. Proper classification based on application type is necessary to measure the smartness of various urban entities.

The document outlines the key architectural components and communication technologies essential for smart cities:

I. Architectural components: Four main components are highlighted: 1) data sensing/actuating, 2) networking and communication, 3) vital components, and 4) cloud/fog computing.

II. Perception or sensing layers: This section discusses physical devices like sensors and actuators that interact with the physical world and measure various quantities such as humidity, temperature, and environmental factors.

III. Networking and communication: Various communication protocols connect devices in smart cities. Short-range protocols like Wi-Fi, ZigBee, and Z-Wave are suitable for limited coverage areas [9]. In contrast, long-range protocols like LoRaWAN, NB-IoT, Sigfox, and LTE are better for broader applications [10]. Each protocol has unique features that suit specific smart city applications.

IV. Evolutions of GSM and LTE: The document also touches on GSM/GPRS and LTE advancements for improving M2M communication efficiency.

Understanding these components and communication protocols helps create efficient, sustainable, livable smart cities by integrating IoT-enabled devices and AI algorithms.

The document discusses the evolution of GSM and LTE technologies to enhance M2M communication efficiency in smart cities:

I. GSM/GPRS enhancements: Efforts focus on improving uplink capacity, extending coverage, and reducing power consumption and complexity of M2M devices. Techniques like Extended Coverage GSM (EC-GSM) use FDMA and CDMA to support more IoT devices on the same frequency band [11].

II. Narrowband Cellular IoT (NB-IoT): This approach uses narrowband channels for downlink and uplink, employing OFDMA for downlink and FDMA for uplink to meet device-specific requirements.

III. LTE evolutions: LTE Rel-11 and Rel-13 focus on handling many IoT devices with reduced capabilities, improving power saving, and enhancing narrowband transmission for M2M communication. Small-cell technology in LTE augments bandwidth and supports low-power, cost-effective devices, making it suitable for various smart city applications.

These advancements aim to create efficient, resilient, low-power communication networks essential for smart city infrastructure.

Integrating IoT and AI in smart city infrastructure presents transformative opportunities for urban development. By leveraging AI-driven analytics and IoT-enabled communication networks, cities can enhance sustainability, efficiency, and overall quality of life for residents. The combination of smart sensors, machine learning algorithms, and 5G networks allows for real-time monitoring, predictive maintenance, and automated decision-making in essential services such as transportation, energy management, and waste disposal.

Despite the numerous benefits, challenges such as data privacy, security, and ethical considerations must be addressed to ensure responsible deployment. Future advancements in AI, IoT, and wireless communication will continue to shape the evolution of smart cities, making them more interconnected and intelligent. Policymakers, researchers, and technology developers must collaborate to establish regulatory frameworks that promote innovation while safeguarding citizens' rights and security.

This study underscores the potential of AI-powered smart city systems in fostering sustainable urban development. With continued research and investment, smart cities will become more resilient, adaptive, and capable of meeting the needs of future generations.

We want to express our sincere gratitude to all the researchers, engineers, and professionals who have contributed to the advancements in IoT and AI technologies. Their innovative work and dedication have significantly propelled the development of smart cities, making urban environments more efficient, sustainable, and livable. Special thanks to the institutions and organizations that have supported this research, providing the necessary resources and platforms for collaboration. We also appreciate the valuable insights and feedback from peer reviewers, which have greatly enhanced the quality of this work. Lastly, we acknowledge the continuous support from our families and colleagues, whose encouragement has been instrumental in completing this study.

KIIT university supported this research; the funding provided by KIIT university was instrumental in facilitating the research and development of AI-powered smart city infrastructure with integrated IoT systems. We also acknowledge the support from KIIT university, which provided additional resources and infrastructure necessary for this study. The collaboration between KISS and KIIT has been crucial in advancing our understanding and implementation of smart city technologies.

The data that support this study's findings are available from the Aman Kumar Sharma upon reasonable request. The datasets generated and analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the Aman Kumar Sharma upon reasonable request. Additionally, some data may be subject to third-party restrictions and require permission from the respective data holders.

The author declare that there are no conflicts of interest regarding the publication of this paper. The research was conducted independently, and no financial or personal relationships influenced the outcomes of this study. All funding sources and affiliations are transparently disclosed, ensuring the integrity and impartiality of the research findings.

This statement ensures transparency and maintains the credibility of the research by declaring that the authors have no conflicting interests that could have influenced the study’s results.