Mental health greatly affects the quality of life. The ability to detect and classify multiple levels of stress is therefore imperative. The aim of this work is to develop machine learning models for detection and multiple level classification of stress through ECG and EEG signals for both unspecified and specified genders. The models for the detection of stress from ECG are developed for real-world use, while the models based on ECG and EEG for the detection and multiple level classification of stress are devised towards clinical use.