A processor-implemented method for assessment of cognitive workload based on breathing pattern of a person, the method comprising: acquiring a plurality of physiological data from the person, wherein a plurality of physiological sensors is adapted to acquire the plurality of physiological data while the person is performing a pre-assigned activity; extracting a plurality of breathing signals from the received plurality of physiological data, wherein a breathing signal extractor in an extractor of a cognitive workload assessment unit is adapted to extract the plurality of breathing signals; extracting a plurality of breathing pattern features from the extracted plurality of breathing signals, wherein a breathing pattern extractor in the extractor of the cognitive workload assessment unit is adapted to extract the plurality of breathing pattern features; selecting a subset of breathing pattern features from the extracted plurality of breathing pattern features based on a feature selection method including Boruta package; generating a personalized training model and a dynamic classifier for the person based on the selected subset of breathing pattern features; acquiring actual plurality of physiological data, wherein the plurality of physiological sensors adapted to acquire the actual plurality of physiological data while the person is performing the pre-assigned activity; extracting plurality of actual breathing signals from the received actual plurality of physiological data, wherein the breathing signal extractor in the extractor of the cognitive workload assessment unit is adapted to extract the plurality of actual breathing signals, wherein the plurality of actual breathing signals are extracted/reconstructed based on normalization techniques using filters that include 4 th order Chebyshev IIR filter with a cutoff frequency of 0.1 Hz-0.5 Hz; extracting plurality of actual breathing pattern features from the extracted actual plurality of breathing signals, wherein the breathing pattern extractor in the extractor of the cognitive workload assessment unit is adapted to extract the plurality of breathing pattern features; classifying the actual breathing pattern features of the person based on the cognitive workload using the personalized training model, wherein the cognitive workload indicate the amount of mental effort by the person while performing the pre-assigned activity, and wherein assessing the cognitive workload ensures effective and efficient performance standards; and displaying the classified actual breathing pattern features. acquiring a plurality of physiological data from the person, wherein a plurality of physiological sensors is adapted to acquire the plurality of physiological data while the person is performing a pre-assigned activity; extracting a plurality of breathing signals from the received plurality of physiological data, wherein a breathing signal extractor in an extractor of a cognitive workload assessment unit is adapted to extract the plurality of breathing signals; extracting a plurality of breathing pattern features from the extracted plurality of breathing signals, wherein a breathing pattern extractor in the extractor of the cognitive workload assessment unit is adapted to extract the plurality of breathing pattern features; selecting a subset of breathing pattern features from the extracted plurality of breathing pattern features based on a feature selection method including Boruta package; generating a personalized training model and a dynamic classifier for the person based on the selected subset of breathing pattern features; acquiring actual plurality of physiological data, wherein the plurality of physiological sensors adapted to acquire the actual plurality of physiological data while the person is performing the pre-assigned activity; extracting plurality of actual breathing signals from the received actual plurality of physiological data, wherein the breathing signal extractor in the extractor of the cognitive workload assessment unit is adapted to extract the plurality of actual breathing signals, wherein the plurality of actual breathing signals are extracted/reconstructed based on normalization techniques using filters that include 4 th order Chebyshev IIR filter with a cutoff frequency of 0.1 Hz-0.5 Hz; extracting plurality of actual breathing pattern features from the extracted actual plurality of breathing signals, wherein the breathing pattern extractor in the extractor of the cognitive workload assessment unit is adapted to extract the plurality of breathing pattern features; classifying the actual breathing pattern features of the person based on the cognitive workload using the personalized training model, wherein the cognitive workload indicate the amount of mental effort by the person while performing the pre-assigned activity, and wherein assessing the cognitive workload ensures effective and efficient performance standards; and displaying the classified actual breathing pattern features.