Space weather forecasting is essential to protect power and space based infrastructure. Operational models rely on current nearside photospheric magnetograms to forecast the solar flares, and to understand propagation of Coronal Mass Ejections. In this current work, we attempted to forecast the photospheric magnetograms for next few days, that can be used as inputs for space weather prediction models. We integrated farside observations to assist the model in learning the emergence of new active regions. In this work, we propose a U-Net inspired deep learning model that takes a full solar rotation of magnetograms as input, along with farside seismic maps, to forecast full-disk photospheric magnetograms over the next few days. Temporal evolution can be accounted for by treating sequential magnetograms as additional input channels, enabling the network to learn the spatiotemporal evolution of the magnetic field. Since farside seismic maps are inherently noisy, threshold based saturation to phase shift is applied to extract stable signals of strong magnetic regions. Initial results demonstrate that the model is capable of reproducing large scale transport processes, such as differential rotation, and can preserve the polarity structure of magnetic fields.