Computer Aided Diagnosis enabled by machine learning has revolutionized the way medical industry operates. Medical Imaging has provided a convenient and hassle free diagnosis methods for medical treatment. Medical Imaging has its roots in all spheres of healthcare. In the recent times, availability of quality digital data in medical field along with convergence of various technological tools resulted in exponential growth in various areas including medical industry. Deep learning has emerged as a subset of machine learning with automated feature extraction abilities ensuring at par or higher accuracy as compared to the medical experts. An accuracy of 99.3% and 100% is achieved in classification of individuals suffering for Alzheimer’s disease with respect to the normal individual and with mild cognitive impairment respectively reinforcing the potential of deep learning tools. With the increasing availability of multi-modal imaging data, the need for churning and extracting the key information becomes the key priority for automation and big data precisely performs the same thereby enabling interpretation based personalized imaging and discovering imaging biomarkers. Finally, these two techniques can only be efficient if there is high end computing power and Graphics Processing Unit (GPU) enabled parallel processing has provided the required platform. However, there still exists challenges like lack of annotated data, variety of modalities, varied sources of modalities, variation in class label and uncertainty in the deep learning black box. To address all these issues, this paper aims at exploring the breadth and depth of the outreach of medical imaging ranging from classification, segmentation, abnormalities detection, motion detection, image reconstruction and pharmacological imaging as an assisting tool including the challenges and the future scope.