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This paper attempts to reconstruct and optimize a DWDM system by avoiding distortions such as Four-Wave Mixing (FWM) and high signal distortion due Inter-Channel Interference (ICI) using supervised and unsupervised learning approaches. FWM in high channel DWDM system reduces the network flexibility, transmission capacity and increases computational difficulties. The ICI and Signal distortion that affects spectral efficiency, increases network latency, leading to undesirable data re-transmission. To solve these above stated issues, the DWDM system necessitates optical variables optimization using supervised and unsupervised regression learning. In this paper, we reconstruct the DWDM system design using supervised and unsupervised regression learning approaches, which are used to identify, correlate, and optimize the FWM influencing optical parameters. Furthermore, trained datasets are generated …