Art is a skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Humans have mastered the skill to create art but there lacks the algorithmic basis of this process and there exists no any artificial system with similar capabilities. Artistic Neural Style transfer is an automated system which introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. This approach helps to transfer that separates style from the content of an image by considering different layers of a neural network. An artistic images of high perceptual quality is created by using neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm. The neural algorithm used in my project is an optimization technique that takes three images, a content image, a style reference image(such as artwork by a famous painter), and the input image to style and blend them together such that the input image is transformed to look like content image, but ‘painted’ in the style of the style image. The system will transform the base input image by minimizing the content and style distances (losses) with backpropagation, creating an image that matches the content of the content image and the style of the style image.