In recent years, road accidents have become a growing threat to public safety, primarily due to the high stress levels and fatigued mental state of drivers. Monitoring driver stress and predicting when levels may become abnormal could play a vital role in reducing hazards on the road. This study explores machine learning (ML) techniques to process and analyze physiological signals such as heart rate (HR), galvanic skin response (GSR), electromyogram (EMG), electrocardiogram (ECG), and respiratory (RESP) rate, with the aim to train and develop a model to recognize stress patterns as well as predict aspects of stress. Various ML models (i.e., random forest (RF), decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), AdaBoost) are used to classify the stress level. The proposed work performs various pre-processing steps to extract important features such as average value, resting value, and standard deviation for analyzing the variability of the signals, considering segmented windows. This work evaluates the performance of various ML models on the benchmark dataset “drivedb”and the RF technique shows improvement compared to existing models with an accuracy of 98.97%. The model analyzes physiological signals and provides higher performance for detecting driver stress compared to state-of-the-art techniques, which will be able to contribute to safer roads.