###### Abstract  
Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3 GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring. 24 September 2  
3% of a nation's GDP [2]. The United Nations General Assembly has established a goal to reduce road traffic deaths and injuries globally by half by 2030 as part of this global crisis management plan [3,4]. As developments in telecommunication technologies have occurred, especially with the widespread integration of wireless communication systems, there has been a growing focus on the integration of sensors with wireless antennas that have the capacity to convey data over considerable distances [5, 6, 7, 8, 9]. This synthesis has facilitated the acquisition of vital information from inaccessible regions, thereby establishing a foundation for improved communication protocols and safety initiatives. In the contemporary milieu of fifth-generation (5G) mobile communication and its subsequent developments, the advent of massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) has markedly increased the connectivity and dependability of Internet of Things (IoT) devices, thereby reinforcing communication frameworks across various sectors, including transportation and healthcare [10, 11, 12, 13]. The advent of 5G networks has facilitated the introduction of a multitude of innovative features pertinent to Vehicle-to-Everything (V2X) communication [14, 15]. The releases from the 3 rd Generation Partnership Project (3 GPP), specifically Release 16 and Release 17, have inaugurated the New Radio V2X (NR-V2X) standard [14], which encompasses SideLink (SL) communication. This technological advancement permits direct radio communication between devices, including vehicles and personal electronic devices, eliminating the need for roadside units to facilitate data transfer. This progress markedly enhances vehicular ad hoc network (VANET) communication, addressing the escalating requirements for safety, commercial applications, and infotainment services [16].