Personalized driving assistance, security, and usage-based insurance models all depend on driver identification in intelligent transportation systems. Conventional techniques for identifying drivers primarily rely on biometric or manually engineered feature data, which may not adequately capture the nuances of unique driving actions. Using time-series driving data, including vehicle speed, steering angle, and acceleration, we present a unique contrastive learning-based method for driver identification in this study. The suggested model uses contrastive learning to compare driving sessions from the same driver to those of other drivers to learn how to distinguish between them. Rather than requiring manual feature extraction, our method automatically extracts strong, high-dimensional representations of driving behavior. Concerning driving sequences, the contrastive learning framework aims to minimize the similarity between sequences from different drivers and maximize the similarity between sequences from the same driver. When applied to situations with various driving behaviors and a small amount of labeled data, our strategy outperforms more conventional classification techniques in accuracy. The suggested model offers a scalable and effective solution for real-world applications, as demonstrated by experimental findings on driving publicly available datasets. This model also greatly increases driver identification performance. We applied seven machine learning classification models. The Random Forest Classifier obtained the highest accuracy of 93.57%.