eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-02-20 18 March Spl Edition 245 255 10.13005/bpj/3085 64270 article Sangita Ajit Patil 1 Ajay Namdeorao Paithane 2 Department of Electronics and Telecommunication, Faculty at Pimpri Chinchwad College of Engineering (PCCOE) and Research Scholar at JSPMs Rajarshi Shahu College of Engineering, Savitribai Phule Pune University (SPPU), Pune, India. Department of Electronics and Telecommunication, Faculty at Dr. D.Y.Patil Institute of Engineering Management and Research (DYPIEMR), Savitribai Phule Pune University (SPPU), Pune, India Stress affects mental and physical health, contributing to cardiovascular diseases and cognitive disorders, and early detection plays a crucial role in mitigating these risks. This study enhances stress detection by analyzing electroencephalography (EEG) signals from the DEAP ( A Database using Physiological Signals) data set and electrocardiogram (ECG) signals from the WESAD (Wearable Stress and Affect Detection) data set, with EEG offering a cost-effective solution and ECG providing detailed cardiovascular insights. It compares individual sensor analysis with multi-sensor fusion, demonstrating that fusion improves accuracy, as the ECG model achieves 91.79% accuracy, the EEG model reaches 96.6%, the feature-level fusion model achieves 98.6%, and the score-level fusion model achieves 97.8%. Using the Archimedes Optimization Algorithm (AoA) and Analytical Hierarchical Process (AHP) for feature selection and a hybrid Deep Convolutional Neural Network-Long Short-Term Memory (DCNN-LSTM) model for processing, the study highlights the effectiveness of a multi modal approach for real- time, accurate stress monitoring in clinical and industrial settings. It also integrates additional modalities and refines methods to enhance the system further, positioning AI-driven multimodal systems as powerful tools for early intervention and improved mental health management. https://biomedpharmajournal.org/vol18marchspledition/ai-driven-multimodal-stress-detection-a-comparative-study/ Archimedes optimization algorithm (AOA)   Deep Convolution Neural  Network (DCNN) Electrocardiogram (ECG) Electroencephalography (EEG) Long Short Term Memory (LSTM)