Ever since the originating era of digital civilization, threat to our belongings and information is increasing at alarming rate. Booming and well-established car lending organization like Taxify, Ola, Uber etc. and bank chest cash replenishing vehicle need a system, which provides additional Tracking Schema over the zone without implantation of Global Positioning System (GPS) for additional security which leads to a heavy load in security messages. To surge security, periodic monitoring of vehicles must be entrenched. Commercial and corporate risk demands 24x7 surveillance that even holds true for vehicular tracking. With escalation in modern sophisticated vehicular epoch, an exceedingly tracking tool is needed for clients who are in ardent need to avoid such problematical chaos of vehicular insecurity. According to Aalsalem et al. (2017) the substantial practice of monitoring the vehicle with layman-friendly dimension is greeted by current community. As discussed by Ogudo et al. (2019) notification at any topographical location can be witnessed by utilizing a webpage support which is accessible through a remote desktop or even in handy smartphone. As discussed by the following authors Khalaf et al. (2019), Adam et al. (2020), Khalaf et al. (2020) and Li et al. (2020), remote sensing of vehicular network is achieved effectively with the implementation of Artificial Intelligence and Machine learning (AI-ML) grid, which is connected in the form of Wireless Sensor Network (WSN). According to Salman et al. (2019), the implementation of intelligent WSN should be made long lasting with the stable functioning algorithm to increase the life span of node or inter node in the WSN and it should be made intelligent. The proposed CVT-AIML-IoT consist of Enhanced Wireless Sensor Network Module (EWSNM) with the combination of data communication module to confine the incoming signals which are frequently watched by connected vehicular in making practical and effective use of Active Radio Frequency ID Sensing and Internet of Things (IoT) Technology which is secured by the dynamic allocation of Artificial Intelligence and Machine learning algorithm for pin generation. As elucidated by Abdulsahib et al. (2018) IoT is integrated with cloud database for effective data processing for the monitored data gathered from the WSN network. Replica of data center of data is created in the cloud to maintain the fail-safe mechanism with the help of data mirroring implementation. CVT-AIML-IoT functions in the environmental safe frequency range of 434 MHz, which is an acceptable frequency range of short range communication. As utilized by the following researchers Adaramola et al. (2020) and Sanda et al. (2020) for the existing monitoring application with over exposure of GPS and GSM module for daily tracking purpose leads to memory related ailments due to its strong ionizing radiation which is emitted. In-order to validate the practicability of the proposed CVT-AIML-IoT, it is implemented on real time as a tracking model which functions in the human friendly operating frequency range of 434 Mhz. Vehicular identification entry processing, Zonal details mapping in accordance with topographical zones and time-stamping processing with entry date and time are made customized with the CVT-AIML-IoT Web User Interface (WUI) to establish the secure and robust way of tracking which is environmental safe due to its acceptable operating frequency.