A processor implemented method comprising: capturing a cognitive task evoked pupillary response in the form of raw pupil size data associated with a subject and metadata associated with the raw pupil size data, wherein the capturing is based on remote eye tracking; pre-processing the captured raw pupil size data to obtain filtered data, characterized in that: performing frequency domain analysis on the filtered data to obtain cognitive load L(t) by: segmenting the filtered data to obtain baseline and trail portions based on time stamps extracted from the metadata associated with the raw pupil size data, computing mean frequency f ( ω) of the filtered data based on (i) number of frequency bins n, (ii) frequency band ω i of each of the frequency bins i, and (iii) frequency f ω i and energy density I ω i associated with each of the frequency bins as given below f ω = ∑ i = 0 n − 1 I ω i f ω i ∑ i = 0 n − 1 I ω i computing a cognitive load L(t) based on the mean frequency f(ω) and power p(ω) corresponding to the mean frequency f(ω) as given below L t = p ω ∗ f ω wherein the cognitive load L(t) is computed for trial blink portion for low and a high cognitive load tasks, wherein said cognitive load tasks are mental addition tasks using ten single digit- numbers from 0-5 for the low cognitive load task and using ten numbers from 6-19, excluding numbers 10, 11, 15, for the high cognitive load task; computing a discriminating index d i per trial portion i, as given below d i = 1, D High i > D Low i 0, otherwise, where D is the cognitive load L(t) computed for high and low cognitive tasks, and computing a total discriminating index, TDI evaluate separation of the low and high cognitive tasks as given below TDI = 1 N ∑ i = 1 N d i × 100. capturing a cognitive task evoked pupillary response in the form of raw pupil size data associated with a subject and metadata associated with the raw pupil size data, wherein the capturing is based on remote eye tracking; pre-processing the captured raw pupil size data to obtain filtered data, characterized in that: performing frequency domain analysis on the filtered data to obtain cognitive load L(t) by: segmenting the filtered data to obtain baseline and trail portions based on time stamps extracted from the metadata associated with the raw pupil size data, computing mean frequency f ( ω) of the filtered data based on (i) number of frequency bins n, (ii) frequency band ω i of each of the frequency bins i, and (iii) frequency f ω i and energy density I ω i associated with each of the frequency bins as given below f ω = ∑ i = 0 n − 1 I ω i f ω i ∑ i = 0 n − 1 I ω i computing a cognitive load L(t) based on the mean frequency f(ω) and power p(ω) corresponding to the mean frequency f(ω) as given below L t = p ω ∗ f ω wherein the cognitive load L(t) is computed for trial blink portion for low and a high cognitive load tasks, wherein said cognitive load tasks are mental addition tasks using ten single digit- numbers from 0-5 for the low cognitive load task and using ten numbers from 6-19, excluding numbers 10, 11, 15, for the high cognitive load task; computing a discriminating index d i per trial portion i, as given below d i = 1, D High i > D Low i 0, otherwise, where D is the cognitive load L(t) computed for high and low cognitive tasks, and computing a total discriminating index, TDI evaluate separation of the low and high cognitive tasks as given below TDI = 1 N ∑ i = 1 N d i × 100. segmenting the filtered data to obtain baseline and trail portions based on time stamps extracted from the metadata associated with the raw pupil size data, computing mean frequency f ( ω) of the filtered data based on (i) number of frequency bins n, (ii) frequency band ω i of each of the frequency bins i, and (iii) frequency f ω i and energy density I ω i associated with each of the frequency bins as given below f ω = ∑ i = 0 n − 1 I ω i f ω i ∑ i = 0 n − 1 I ω i computing a cognitive load L(t) based on the mean frequency f(ω) and power p(ω) corresponding to the mean frequency f(ω) as given below L t = p ω ∗ f ω wherein the cognitive load L(t) is computed for trial blink portion for low and a high cognitive load tasks, wherein said cognitive load tasks are mental addition tasks using ten single digit- numbers from 0-5 for the low cognitive load task and using ten numbers from 6-19, excluding numbers 10, 11, 15, for the high cognitive load task; computing a discriminating index d i per trial portion i, as given below d i = 1, D High i > D Low i 0, otherwise, where D is the cognitive load L(t) computed for high and low cognitive tasks, and computing a total discriminating index, TDI evaluate separation of the low and high cognitive tasks as given below TDI = 1 N ∑ i = 1 N d i × 100.