Stress is a complex physiological and psychological response that can significantly impact an individual’s health, making its accurate detection and management crucial for well-being. This study investigates the effectiveness of multimodal machine learning frameworks for stress detection by comparing three classifiers: Random Forest, Gradient Boosting, and Gaussian Naive Bayes. The results show that Random Forest is the most effective model, achieving highest accuracy by optimally integrating 15 various physiological signals as EMG (Electromyogram), EDA (Electrodermal Activity), TEMP (Body temperature), and ACC (Accelerometers) from WESAD dataset which is publicly available. Gradient Boosting performs well, especially with chest-EDA data, but falls short of Random Forest’s accuracy. Gaussian Naive Bayes shows lower performance due to its limitations in handling multimodal data. The research highlights the value of combining data from multiple sensors for a more precise analysis of stress. It also introduces an incremental fusion strategy that builds unimodal models step-by-step to analyze each sensor’s contribution. The study concludes that multimodal frameworks significantly outperform single-modal approaches, offering a more innovative and effective method for stress detection.