###### Abstract
In this study, the recent literature was observed and reviewed concerning early detection and real-time monitoring of diabetes, which shows that most vehicle crashes are due to the drivers having diabetes. This area has received significant attention by finding the relationship between hypoglycemia and vehicle crashes [7; 8]. Vehicle crashes could have been avoided if the driver had been warned ahead of time. Machine learning plays a significant role in the early prediction and detection of diabetes. Recent advancements in VANET and ubiquitous computing alongside novel wearable biosensors give constant observation of a patient's condition and offer another point of view on diabetes management. Useful suggestions from the surrounding vehicles in the VANET environment could be vital to reducing the risk of vehicle crashes and improving the safety of the drivers. The intelligent vehicle having an on-board unit (OBU) and application unit (AU) is outfitted with VANET for obtaining, conveying, and sending the necessary information to another vehicle or V-Server [9; 10; 11]. Our aim in this research is to propose an effective solution for human biological problems and efficiency by utilizing an intelligent vehicular system and advanced CGM sensors. This proposed solution will help discover and monitor diabetes by using the existing datasets available and from the digitalization of data obtained from different experiments in the lab or by existing advanced CGM devices. We proposed an evaluation of VANET for collecting and monitoring real-time data of diabetic drivers, which provides safe practice for driving and reduces the risk of vehicle crashes among individuals. The intelligent vehicle is outfitted with VANET for obtaining, conveying, and sending the necessary information to another vehicle or V-Server. OBU is embedded in the vehicle for data collection from the patient's medical sensor. OBU is closely associated with the processing unit for intelligent decision-making when the blood glucose (BG) of the patient varies from threshold values (80-100 mg/dL). The accepting data through the vehicle would be stored in the V-Server and analyzed for the next execution level. As per the choice, it would communicate to specialists/physicians, contact VANET members for the nearest help, or contact the nearest emergency service using GPS. Further, the proposed system also embedded the VANET environment, which could be beneficial in medical emergency scenarios to increase the security of the driver, passengers, and pedestrians on the road. Figure 3 illustrates the VANET environment. The proposed system reduces the risk of vehicle crashes caused by drivers having diabetes by providing infrastructure for real-time monitoring of diabetes through VANET that will respond according to the situation, which helps to save lives, time, and cost. We also provide a novel approach for the early prediction and detection of diabetes as well as distinguish different types of diabetes. The proposed system increases the efficiency and accuracy of advanced glucose monitoring sensors by integrating human genetics information. Our proposed methods outperform by achieving accuracy up to 95.0%, precision up to 95.4%, recall up to 95.0%, F1-score up to 95.0%, specificity up to 92.7%, sensitivity up to 96.6%, MCC up to 90.3%, and AROC up to 99.5%.