Technology is evolving in rapid speed. Machines has started to learn on its own. The use of Machine Learning in different field has been increasing day by day. Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image. It can be applied to a wide range of applications, such as season transfer, style transfer, and photo enhancement. This project shows how an algorithm can be implemented for image-to-image translation. This project helps artist to use their mobile devices to draw a sketch and help them to visualize how their art (Sketch) looks like in real world i.e., to convert imagination to reality. The main goal of the project is to use machine learning to study the image and convert the image form one domain (sketch) to another domain (real look alike human face). In this project, the model has been trained under various condition given different value of generator loss to choose the model for the application. In this project Pix2pix algorithm has been used for image-to-image translation. PSNR value is taken into consideration for quantitative analysis of generated image