## 1 Introduction  
The need for smart transport systems is growing rapidly. The existing transport system is ladened with recurrent accident issues, coupled with a growing opportunity cost in regard to traffic congestion in industrialized and developed cities [1] and safety concerns by users. The VANET system was ushered in to address these concerns and was able to improve the performance of vehicular networks and overcome their inherent limitations. Growing demand and technological breakthroughs over the years have now challenged the VANET system and have ushered in the blockchain-based Internet of Vehicles (IoV) [2]. The exponential advancement in the development and deployment of intelligent and connected vehicles has further necessitated and deepened the demand for IoV systems [3; 4]. The IoV is a branch of IoT that is dedicated to vehicular communication and interaction with the internet. IoV is a growing innovation and researchers project that the number of IoT devices in use will reach 75 billion by 2025 [5; 6]. This surge in usage signals challenges such as resource management, security etc. The blockchain is a powerful technology that acts as a distributed ledger, integrated to provide a solution to the challenges in the IoV [2; 7; 8; 9]. One of the main benefits of integrating blockchain technology (BC) with the IoV is that they could contribute to improving the vehicular network environment by providing credit support for intelligent transport systems (ITS) [8]. BC adoption globally is incremental, and experts project that its market value would reach USD 39.7 billion by 2025 [10]. This is largely owing to its capabilities in providing features such as interoperability, security, confidentiality, authentication, and overall better performance in the IoV domain [8]. Machine learning offered the most promising strategies to detect unauthorized attacks or access in IoV-based enterprise information systems due to its better accuracy and precision [11]. Its learning capacity makes it adaptable to new forms of security bridge. ML is a subbranch of artificial intelligence (AI) [10; 12] that empowers computers to think,study, and act without any human intervention [13]. ML seeks to mimic the human neuron system in its operation. It learns from input data through a set of neural networks within the architecture and makes decisions based on feedback process [10; 13]. The adoption and application of ML algorithms are usually on a use basis, and they are specific to the required outcome for each application [12]. The combination of BC and ML promises cutting-edge opportunity in many technological domains [14; 15; 16; 17; 12]. The concept of technological convergence birthe Industrial 4.0 [16]. Modern vehicles are now beyond mere thermo-mechanical machines; they are sophisticated hardware and software systems due to combination of IoV connectivity [6]. These have opened innovative business opportunities for manufacturers like Tesla, GM, Ford, and many others to design autonomous/self-driving cars with increased precision and accuracy [18; 19]. Today, vehicles in the IoV system generate a tremendous amount of real-time data, which has given rise to concerns such as decentralization, interoperability issues, and scalability issues [16]. Furthermore, big data demands have increased the required data processing time for ML applications [20]. For efficient system operations, ML and blockchain technology could be integrated to improve operational performance [10; 20; 21]. For instance, blockchain can be adapted for a secure and decentralized platform, where data collection and sharing are screened for privacy and security, which then enables machine learning models to be deployed and executed in a decentralized and distributed manner [20; 21]. These challenges can be addressed through the convergence of BC and ML technologies, thus enabling communication between vehicles, humans, sensors, and road infrastructure [16].