Anxiety-related disorders are the most common mental health disorders of adolescence. Its consequences are even more prevalent as a psychiatric condition among the elderly population. Research has reported a prevalence rate of 3.6% of general anxiety disorder (GAD) among the Indian population. Different anxiety assessment measures are created for different age groups. EDA (Electrodermal Activity) and HRV (Heart-Rate Variability) together can provide a very robust bio-feedback that can be used for the prediction of the autonomic changes associated with anxiety disorders.However, to the best of the researchers' knowledge, whether subjective anxiety can be predicted by objective physiological indices has not been explored before. Hence, the present research aims to investigate whether self-assessed anxiety as reflected by the state-anxiety (Y1) and trait-anxiety (Y2) components of the clinically approved STAI questionnaire are predicted by physiological signals using machine learning algorithms.