The processing and interpretation of medical images through traditional method have been changed now using deep learning algorithms. This paper offers a comprehensive overview of deep learning's applications, advancements, and challenges in medical imaging. By automatically extracting intricate patterns and traits from unprocessed visual data, deep learning has changed the healthcare landscape by enhancing patient care, treatment planning, and diagnostic precision. This paper presents detailed analyses of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and other important deep learning concepts which are being used in medical image analysis. It underlines how these techniques can be applied to handle crucial jobs, including image registration, disease detection, and image segmentation. Deep learning's benefits are clear in its capacity to extract features automatically, recognize intricate patterns, and offer quantitative measurements for medical image interpretation. These developments facilitate personalized medicine, speed up the diagnosis procedure, and provide fresh perspectives on patient situations. But data privacy, interpretability, and generalization still exist, necessitating the cooperation between medical professionals, machine learning experts, and the ethicists.