## 1 Introduction
These sensors collect and transmit data on various parameters, such as temperature, humidity, traffic flow, energy consumption, and air quality. In general, the use of IoT devices generates vast quantities of data [22; 23]. These data subsequently enhance city management and elevate residents' living standards. However, the sheer volume of data generated by IoT sensors can be overwhelming for human operators to analyze and interpret. AI plays a significant role in this aspect by utilizing machine learning (ML) algorithms to analyze vast amounts of data and detect patterns and trends that might be challenging for humans to discern. Analyzing large amounts of complex data can be challenging to accurately determine the most precise and effective course of action [24; 25]. One key area where AI can enhance IoT in smart cities is predictive maintenance. By analyzing data from IoT sensors, AI algorithms can predict when maintenance is needed for city infrastructure, such as bridges and buildings, before a failure occurs. This can help prevent costly and dangerous failures and ensure the city's infrastructure is always in good condition. AI has enabled various applications such as smart water supply, energy management, waste management, and mitigating pedestrian and traffic congestion, noise, and environmental pollution. Most smart city programs and technologies have focused on collecting large amounts of data and creating solutions that tackle the complexities and dynamics of specific applications [26; 27]. AI-powered applications enable the utilization of large volumes of data and knowledge to facilitate decision-making. Around 30% of smart city applications are now significantly integrating AI to enhance urban sustainability, resilience, social welfare, and vitality, including urban transportation solutions. This trend is expected to continue, with an anticipated increase in AI-powered smart city initiatives by 2025 [28]. The rapid expansion of AI-based smart city concepts can be attributed to the relentless drive of researchers, government officials, and urban dwellers to explore new information and methods for building smart cities. The proliferation of automation and AI is becoming increasingly inevitable, with growing demand for their implementation in smart city development [22]. Smart sensor nodes [29] generate large amounts of data associated with various smart city applications and are significantly under-used. Existing Information and Communication Technology (ICT) [30] infrastructure can generate heterogeneous information, which is essential for consolidation. Finally, AI can improve public safety by analyzing data from security cameras, microphones, and other sensors to detect and prevent crime and other security threats. While integrating AI with IoT in smart cities can revolutionize urban development and management, it also raises concerns about privacy, data security, and potential biases in AI algorithms. As such, smart cities must implement strong ethical and regulatory frameworks to ensure these technologies are used responsibly and transparently. The potential threat and concern are not the discussion point of the current review paper. Figure 2 illustrates the communication technologies that are currently available and can be utilized in various applications within a smart city. IoT-based technologies combined with AI in a smart city can be viewed from various perspectives, including smart sensors, communication technologies, and multiple applications. Among the different technologies, the most critical IoT technologies entail the deployment of reliable and robust networking and communication infrastructures to facilitate efficient data and information exchange among the diverse components of smart city services. Smart city IoT services are designed at varying scales, depending on the type of application. They may require diverse networking and communication technologies for their implementation and operation.