The risk of older drivers' involvement in accidents increases with age, and the damages that they get from crashes are more irreparable than middleaged drivers. The current study uses in-vehicle sensing systems to analyze and classify the driving behaviors of older drivers, precisely those aged 65 and older, using unsupervised machine learning techniques. The study utilizes telematics data from in-vehicle sensors over three years to identify patterns and categorize driving behaviors. The primary objective is to develop a framework that can detect patterns indicative of aggressive or cautious driving styles, leveraging Self-Organizing Maps (SOMs) and Deep Embedded Clustering (DEC) methods to reduce the complexity of the data. The research shows the importance of telematics data in understanding driving behaviors and presents unsupervised learning methods, particularly SOM, as effective methods in visualizing and interpreting complex driving patterns. The results indicate that a 5 × 5 grid SOM and combining DEC with K-means are the most effective methods for determining the optimal number of clusters. The clustering analysis reveals two distinct clusters as the optimal outcome, suggesting that the majority of driving behavior among older drivers in the dataset is marked by cautious driving. The methodologies used are generalizable to other demographics and driving conditions, making the findings relevant for broader applications in traffic safety analysis.