Stress has multiple effects on human health. Sensors designed to measure stress and indicate health status by recognizing illnesses or other conditions (e.g., heart problems and blood pressure) have been widely utilized to monitor and characterize this physiological phenomenon. Stress has two response mechanisms: the autonomic nervous system (ANS) and hypothalamic-pituitary-adrenal (HPA) axis. ANS can affect heart rate, breathing rate, skin conductance, blood pressure, and other hemodynamic parameters. Continuous non-invasive blood pressure (cNIBP) measurement, pulse volume, cardiac output, and other hemodynamic parameters are important for stress measurement and health indicators. There is still room for research and development of different approaches to measurement in this area. Very few sensor systems associated with cNIBP have been developed or are currently in progress. Photoplethysmography (PPG), impedance plethysmography (IPG), and ultrasound imaging were performed along with other non-invasive sensors, such as electrocardiography (ECG), cardioseismography (CSG), and ballistocardiography (BCG), to measure hemodynamic parameters. In the HPA axis, stress hormones are the most important measurement from the perspective of cortisol levels. This measurement is also important in general for the health of the subject, especially for good functioning of the axis itself (HPA axis). Sensors have been developed to detect cortisol levels for academic and research purposes. Cortisol levels can be measured in two ways: direct and indirect hormone measurements. Non-invasive direct hormone measurement uses a sensor to evaluate the cortisol levels in sweat. In contrast, indirect measurement uses an increase or decrease in cortisol levels in relation to other substances such as sodium or potassium. Therefore, in the present study, we investigated technologies, methods, and wearable sensors for continuous hemodynamic measurements at the ANS level and cortisol measurements at the HPA axis level. These sensors and measurements are crucial for improving healthcare applications.