Changes in the driving behavior of older drivers can be indicative of conditions of mild cognitive impairment (MCI), which affect their memory and recognition skills on the road. Traditional clinical evaluations cover only a limited subset of cognitively impaired drivers, prompting the need for innovative technologies to monitor the cognitive status of older drivers routinely. In this study, we developed in-vehicle sensing devices capable of capturing vehicular data streams that reveal older drivers’ driving patterns. Using K-means clustering on preprocessed and scaled data, we identified four distinct driver profiles characterized by trip frequency, driving style, and demographic factors. These profiles ranged from active, frequent travelers to sedentary, cautious drivers, with significant differences in trip duration, distance, and vehicle operation metrics such as speed and engine load. A developed random forest model further identified peak hour trips, age, gender, and ambient temperature as significant predictors of MCI, highlighting the complex interplay between lifestyle, driving behaviors, and demographics.