Image Contrast Enhancement for Brightness Preservation main objective is to overcome the problems faced by the existing methods of image contrast enhancement. Image Contrast enhancement is a process that makes the image features stand out more clearly by making optimal use of the colors available on the display or output device using deep learning computer vision concepts. There are several existing methods for image contrast enhancement like Histogram Equalization, Bi-linear interpolation, Contrast Limited Adaptive Histogram Equalization and Dynamic Stretching. We have considered a base paper which proposed dynamic stretching after comparing with the other methods. There are some issues identified with the existing dynamic stretching method: light and brightness levels not being changed and there are no positive results for dusty and low light images. Moreover, it is an iterative process operating on a single intensity channel and using a golden section search algorithm to calculate pixel positions using mean brightness. Our approach tries to address the issues by proposing a new methodology. Our proposed method makes use of dynamic stretching approach but the operation is being done on all [R, G, B] channels and using RGB to HSI conversion and later using HSI to RGB conversion just before producing the enhanced image thus making the proposed method efficient. Histogram Equalization has also been used as a part in our proposed method for preserving brightness and contrast enhancement. Experiments conducted on different images from different domains like medical images, low light images, dusty images and black and white satellite images without haze showed efficient results with brightness preservation while performing image contrast enhancement. Hence our proposed method has overcome the issues faced with the existing image contrast enhancement methods.