###### Abstract
Furthermore, ongoing initiatives by the 3 GPP are directed towards improving communication reliability, extending coverage, minimizing latency, and optimizing power consumption, particularly for devices reliant on battery power [17, 18, 19, 20]. VANETs, a distinct category of mobile ad hoc networks (MANETs), are integral to the functionality of Intelligent Transportation Systems (ITSs) [21, 22, 23, 24]. VANETs facilitate communication between vehicular units and adjacent roadside infrastructure, wherein vehicles serve as mobile nodes within a self-organizing network framework. These networks operate without the necessity for prior coordination, as each vehicle operates as a wireless routing device, thereby enabling interaction with proximate vehicles. In metropolitan contexts, this communicative exchange generally transpires within a radius of 100 to 300 m, with potential extension up to 1000 m on arterial highways [17, 18]. As vehicles transition in and out of communication range, the network undergoes continuous adaptation and reconfiguration, culminating in a fluid and dynamic communicative milieu [19]. Recently, the utilization of artificial intelligence (AI) has increased across diverse domains, encompassing cybersecurity, data analytics, routing, healthcare, and robotics [25, 26, 27]. AI methodologies, including machine learning (ML), deep learning (DL), and swarm intelligence (SI), are increasingly being recognized in the formulation of solutions for vehicular ad hoc networks (VANETs) to mitigate their myriad challenges [28, 29]. Although AI has demonstrated considerable potential in enhancing vehicular communication, additional investigation is imperative to thoroughly harness its capabilities within this sector. A prominent AI methodology that has demonstrated substantial potential within VANETs is K-means clustering. This specific type of unsupervised machine learning technique, which is part of the larger domain of artificial intelligence, is particularly effective in facilitating efficient data exchange among vehicles [30, 31, 32, 33, 34, 35]. Through the implementation of a clustering-oriented topology, K-means categorizes vehicles situated in close geographical proximity, thereby facilitating direct communication within a cluster and inter-cluster data transmission via designated cluster heads. This strategy significantly augments both the scalability and dependability of data exchange within VANETs. Given the promising role of AI, particularly K-means clustering, in improving VANET performance, this paper proposes a multi-layered VANET-enabled ITS framework, driven by unsupervised learning. This framework is designed to address the key challenges in VANET communication and help meet the United Nations' goal of reducing road traffic fatalities by 50% by 2030. Table 1 provides a summary of the literature on road traffic safety, VANETs, and AI applications with identified research gaps. The contributions of this paper are as follows:  
1. Introduction of a novel multi-layered, AI-powered VANET framework for ITS. 2. Exploration of K-means clustering for optimized data interaction among vehicles. 3. Application of unsupervised learning to enhance VANET scalability and reliability.