A processor-implemented method for classification and quantitative estimation of cognitive stress in real-time, the method comprising: obtaining, while a user is engaged in performance of a task, a plurality of non-invasive physiological signals from a plurality of physiological sensors accessible to the user, via one or more hardware processors; identifying a plurality of feature sets from the plurality of non-invasive physiological signals, via the one or more hardware processors, the plurality of feature sets correlating with the cognitive stress experienced by the user while engaging in the performance of the task; and predicting, using a regression model, a stress indicator metric comprising a quantitative estimate of the cognitive stress experienced by the user during the performance of the task, via the one or more hardware processors, wherein quantitative estimate of the cognitive stress experienced by the user is by obtaining the keystroke data during performance of at least one keyboard-based task involving navigating a moving object to a target object out of a plurality of target objects; determining relative position of the moving object with respect to the ultimate target object during each epoch of the keyboard based task; defining for each of the epochs during the keyboard-based task a single value of f(t) at each time instant (t=0 to T, T=duration of epoch) timestamp is logged; computing the values of stress indicator metric based on the equation S = (A - 10B) / T - 0.5, wherein a single value of f(t) at each time instant (t=0 to T, T=duration of epoch) timestamp is logged such that: ƒ t = ƒ t − 1 denotes no keystroke at time t ƒ t = ƒ t − 1 − 1 / x denotes single keystroke in opposite direction ( y o) at time t ƒ t = ƒ t − 1 + 1 / x denotes single keystroke in same direction ( y s) at time t, where A represents rewards for persistence and B represents penalties for indecision wherein A and B are computed using the equations A = ∑ t = 0 T min ƒ T, max ƒ t, ƒ 0 B = ∑ t = 0 T min ƒ t ̅, ƒ 0 + max ƒ t, ƒ T − ƒ T wherein the regression model is trained using the plurality of feature sets, and independently determined quantitative estimates of cognitive stress used as a ground truth to output value of the stress indicator metric, and wherein the ground truth is determined from keystroke data associated with the performance of keyboard-based tasks comprising navigation of one or more moving objects to at least one target from a plurality of targets. obtaining, while a user is engaged in performance of a task, a plurality of non-invasive physiological signals from a plurality of physiological sensors accessible to the user, via one or more hardware processors; identifying a plurality of feature sets from the plurality of non-invasive physiological signals, via the one or more hardware processors, the plurality of feature sets correlating with the cognitive stress experienced by the user while engaging in the performance of the task; and predicting, using a regression model, a stress indicator metric comprising a quantitative estimate of the cognitive stress experienced by the user during the performance of the task, via the one or more hardware processors, wherein quantitative estimate of the cognitive stress experienced by the user is by obtaining the keystroke data during performance of at least one keyboard-based task involving navigating a moving object to a target object out of a plurality of target objects; determining relative position of the moving object with respect to the ultimate target object during each epoch of the keyboard based task; defining for each of the epochs during the keyboard-based task a single value of f(t) at each time instant (t=0 to T, T=duration of epoch) timestamp is logged; computing the values of stress indicator metric based on the equation S = (A - 10B) / T - 0.5, wherein a single value of f(t) at each time instant (t=0 to T, T=duration of epoch) timestamp is logged such that: ƒ t = ƒ t − 1 denotes no keystroke at time t ƒ t = ƒ t − 1 − 1 / x denotes single keystroke in opposite direction ( y o) at time t ƒ t = ƒ t − 1 + 1 / x denotes single keystroke in same direction ( y s) at time t, where A represents rewards for persistence and B represents penalties for indecision wherein A and B are computed using the equations A = ∑ t = 0 T min ƒ T, max ƒ t, ƒ 0 B = ∑ t = 0 T min ƒ t ̅, ƒ 0 + max ƒ t, ƒ T − ƒ T wherein the regression model is trained using the plurality of feature sets, and independently determined quantitative estimates of cognitive stress used as a ground truth to output value of the stress indicator metric, and wherein the ground truth is determined from keystroke data associated with the performance of keyboard-based tasks comprising navigation of one or more moving objects to at least one target from a plurality of targets.