A processor implemented method comprising: estimating, by an in-silico cardiovascular hemodynamic model via one or more hardware processors, in each cardiac cycle of an electrocardiogram signal, cardiac parameters based on morphology of the ECG signal associated with a subject, wherein the cardiac parameters include a continuous heart rate and a set of compliance parameters, and wherein estimating the set of compliance parameters is based on: (i) a set of PQRST amplitudes; and (ii) time-instances, ( a p j t p j a q j t q j a r j t r j a s j t s j a t j t t j; j E m) for a j th cardiac cycle (∀ j ∈ m) of the ECG signal, wherein the continuous HR is estimated, when the ECG signal is missing or noisy, and is represented as: HR t = { h ae end + w t during resting state 1 − t + 1 e − t τ k h ae 0 + w t during exercising state where t is time considered over a complete cardiac cycle, h ae ( end) is the HR at a last instance of capturing of the ECG signal in the resting state and h ae is the HR at a first instance of the capturing of the ECG signal in the exercise state, wherein the subject is initially in the resting state and then performs exercise to reach the exercise state, w ( t) = N 0 σ 2 is white-noise with zero-mean, variance of σ 2 = 9.26, and τ k = 50 sec defines a time constant, and wherein the σ 2, and the τ k are learnt empirically through the ECG signal using linear regression; generating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a set of compliance functions using the estimated cardiac parameters; sequentially activating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a plurality of cardiac chambers of the in-silico cardiovascular hemodynamic model, in a synchronized manner, using the generated set of compliance functions; and estimating blood pressure of the subject, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, using the estimated cardiac parameters and the sequentially activated plurality of cardiac chambers to simulate the in-silico cardiovascular hemodynamic model in line with a human cardiovascular system of the subject, wherein the in-silico cardiovascular hemodynamic model is driven by the ECG signal associated with the subject. estimating, by an in-silico cardiovascular hemodynamic model via one or more hardware processors, in each cardiac cycle of an electrocardiogram signal, cardiac parameters based on morphology of the ECG signal associated with a subject, wherein the cardiac parameters include a continuous heart rate and a set of compliance parameters, and wherein estimating the set of compliance parameters is based on: (i) a set of PQRST amplitudes; and (ii) time-instances, ( a p j t p j a q j t q j a r j t r j a s j t s j a t j t t j; j E m) (i) a set of PQRST amplitudes; and (ii) time-instances, ( a p j t p j a q j t q j a r j t r j a s j t s j a t j t t j; j E m) for a j th cardiac cycle (∀ j ∈ m) of the ECG signal, wherein the continuous HR is estimated, when the ECG signal is missing or noisy, and is represented as: HR t = { h ae end + w t during resting state 1 − t + 1 e − t τ k h ae 0 + w t during exercising state where t is time considered over a complete cardiac cycle, h ae ( end) is the HR at a last instance of capturing of the ECG signal in the resting state and h ae is the HR at a first instance of the capturing of the ECG signal in the exercise state, wherein the subject is initially in the resting state and then performs exercise to reach the exercise state, w ( t) = N 0 σ 2 is white-noise with zero-mean, variance of σ 2 = 9.26, and τ k = 50 sec defines a time constant, and wherein the σ 2, and the τ k are learnt empirically through the ECG signal using linear regression; generating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a set of compliance functions using the estimated cardiac parameters; sequentially activating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a plurality of cardiac chambers of the in-silico cardiovascular hemodynamic model, in a synchronized manner, using the generated set of compliance functions; and estimating blood pressure of the subject, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, using the estimated cardiac parameters and the sequentially activated plurality of cardiac chambers to simulate the in-silico cardiovascular hemodynamic model in line with a human cardiovascular system of the subject, wherein the in-silico cardiovascular hemodynamic model is driven by the ECG signal associated with the subject.