Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this talk, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to astronomical images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of stack images from single SDSS images, (2) Generation of SDO/EUV images from SDO/HMI magnetograms, (3) Generation of farside magnetograms from STEREO/EUVI images, (4) Generation of EUV & X-ray images from Carrington sunspot drawing, and (5) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. We will discuss several scientific application of such an image translation method scuh as the sunspot evolution from backside to frontside. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.