Compared with space networks, new aerial platforms such as Low Altitude and High Altitude Platforms (LAPs and HAPs) have a considerable advantage with reduced transmission distances, low deployment time and costs, and reduced communication channel losses. Therefore LAPs and HAPs can be excellent solutions for complimenting the VN for providing new innovative and intelligent services to the end-users. Every connected vehicle in a VN is equipped with several sensors, able to generate tones of data (i.e., big data) in real-time. These data can be analyzed and exploited for improving the quality of VNs services and applications [14]. Recently machine learning (ML) techniques are used for solving challenging problems over wireless networks [15, 16]. With new hardware technologies and the availability of a large amount of data through IoT devices, ML research is grown fast. Several new ML tools and techniques have been developed and utilized for solving real-world problems on a daily basis. The use of new innovative ML-based solutions for analyzing the vehicular data for improving the vehicular environment can be beneficial [17]. However, proper infrastructures with communication and computing resources are required, for embedding the ML-based solutions into VNs, failure of which can introduce higher process costs (i.e., cost induced by ML model training, inference, etc). With limited resources and dynamic movements in VNs, it is challenging to implement ML techniques. The EC resources can be exploited for integrating the ML-based solution techniques into VNs. Traditional ML approaches such as centralized ML require VUs to transmit their data to the centralized, more powerful servers [18]. However, such an approach can introduce higher costs in terms of communication latencies and energy requirements. With resource limitations and critical service requirements performing centralized ML over VN can be challenging. On the other hand, new learning approaches such as Federated Learning (FL), VUs are able to perform the training operations by themselves [19]. In each FL round, VUs perform the training operations to learn local ML model parameters, to be then sent towards the centralized server. After receiving all VUs parameters from its coverage area, the server is able to perform an averaging operation for creating a global model to be broadcast back to the VUs. Thus, the VUs can reduce the data communication cost by performing local training operations and also exploit the presence of other VUs, by learning experiences/data through the averaging. For these reasons, recently, FL-based solutions are preferred for VN application when solving challenging problems [19, 20, 21]. For benefiting from the collaborative learning into FL, a server platform with better coverage characteristics and channel conditions is required. NTN platforms, such as HAPs, can assist VN in implementing an efficient FL process with their better coverage characteristics, moderate transmission distances, and better channel conditions. FL and other ML techniques will be described in more detail in Section 3. Therefore, there is clear scope for integrating NTN layers, especially air network-based EC platforms, with ground-based EC resources, into VNs. Such an approach can solve the limited capacity and coverage problem of conventional ground-based EC platforms and can provide more intelligent services and applications with stringent requirements. Therefore, we aim to provide a novel multiple EC platforms-enabled VN architecture by integrating ground and aerial network-based EC resources. Different enabling technologies, corresponding challenges, and opportunities are discussed in detail. We further analyze several vehicular scenarios and the benefits of using the proposed VN architecture for solving the challenges associated with them.