A processor-implemented method for optimized spike encoding for spiking neural networks for signal reconstruction comprising: receiving a plurality of input signals from a data source, via a one or more hardware processors, wherein each input signal of the plurality of input signals is a time series signal and the data source comprises of a plurality of sensors, and wherein the time series signal is a Mackey-Glass Time series having a non-linear chaotic time-series generated by a delay differential equation; introducing a Gaussian noise into the plurality of input signals, via the one or more hardware processors, to obtain a plurality of Gaussian noise introduced signals; generating a plurality of initial encoded spike trains from the plurality of Gaussian noise introduced signals, via the one or more hardware processors, based on an encoding technique; computing a mutual information, via the one or more hardware processors, between (i) the plurality of Gaussian noise introduced signals and the (ii) the plurality of initial encoded spike trains based on a MI computation technique, wherein the MI is computed across an entire length of an initial encoded spike train from amongst the plurality of initial encoded spike trains, and wherein the mutual information is a measure of an information content within the plurality of initial encoded spike trains generated via encoding of the plurality of input signals; optimizing the mutual information, via the one or more hardware processors, through an optimization technique that maximizes the MI by varying the Gaussian noise in the plurality of Gaussian noise introduced signals; identifying an initial encoded spike train among the plurality of initial encoded spike trains to obtain an optimized spike train, via the one or more hardware processors, wherein the optimized spike train is identified based on a pre-defined criteria defined as determining the initial encoded spike train comprising a maximum of the optimized MI among the plurality of initial encoded spike trains; and training, via the one or more hardware processors, a spiking neural network using the optimized spike train, wherein the SNN is trained to learn temporal dynamics of the optimized spike train, and a post-synaptic trace of the SNN is then fed into a Linear Regression module with corresponding real values from the respective input signal as labels to learn during the training and to reconstruct the data from the unseen parts of the respective input signal during validation. receiving a plurality of input signals from a data source, via a one or more hardware processors, wherein each input signal of the plurality of input signals is a time series signal and the data source comprises of a plurality of sensors, and wherein the time series signal is a Mackey-Glass Time series having a non-linear chaotic time-series generated by a delay differential equation; introducing a Gaussian noise into the plurality of input signals, via the one or more hardware processors, to obtain a plurality of Gaussian noise introduced signals; generating a plurality of initial encoded spike trains from the plurality of Gaussian noise introduced signals, via the one or more hardware processors, based on an encoding technique; computing a mutual information, via the one or more hardware processors, between (i) the plurality of Gaussian noise introduced signals and the (ii) the plurality of initial encoded spike trains based on a MI computation technique, wherein the MI is computed across an entire length of an initial encoded spike train from amongst the plurality of initial encoded spike trains, and wherein the mutual information is a measure of an information content within the plurality of initial encoded spike trains generated via encoding of the plurality of input signals; optimizing the mutual information, via the one or more hardware processors, through an optimization technique that maximizes the MI by varying the Gaussian noise in the plurality of Gaussian noise introduced signals; identifying an initial encoded spike train among the plurality of initial encoded spike trains to obtain an optimized spike train, via the one or more hardware processors, wherein the optimized spike train is identified based on a pre-defined criteria defined as determining the initial encoded spike train comprising a maximum of the optimized MI among the plurality of initial encoded spike trains; and training, via the one or more hardware processors, a spiking neural network using the optimized spike train, wherein the SNN is trained to learn temporal dynamics of the optimized spike train, and a post-synaptic trace of the SNN is then fed into a Linear Regression module with corresponding real values from the respective input signal as labels to learn during the training and to reconstruct the data from the unseen parts of the respective input signal during validation.