Image processing has emerged as a critical technology in modern healthcare, revolutionizing various aspects of medical diagnosis, treatment, and research. This paper presents a comprehensive review of the role of image processing techniques and their applications in healthcare. The paper begins by highlighting the fundamental importance of medical imaging in diagnosing diseases and guiding treatment decisions. It then delves into the various image processing techniques that are employed to enhance and analyze medical images. These techniques include image enhancement, segmentation, registration, and classification. Image enhancement techniques aim to improve the visual quality of medical images by reducing noise, enhancing contrast, and improving spatial resolution. Various filtering algorithms, such as median filtering and wavelet transforms, are commonly employed for noise reduction. Contrast enhancement techniques, such as histogram equalization and adaptive filtering, are used to enhance the visibility of structures within the images. Segmentation plays a crucial role in extracting relevant information from medical images. It involves partitioning an image into meaningful regions, such as organs or lesions. Numerous segmentation algorithms, including thresholding, region-growing, and active contours, have been developed to accurately delineate anatomical structures or identify abnormalities within images. Image registration techniques enable the alignment of multiple images acquired at different times or from different modalities. By aligning images, clinicians can compare changes in a patient's condition over time or fuse complementary information from different modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT). Registration algorithms utilize features such as landmarks, intensity-based methods, or deformable models to achieve accurate alignment. Classification techniques utilize machine learning algorithms to classify medical images into different categories, such as identifying cancerous tumors or classifying diseases based on their severity. Deep learning approaches, particularly convolutional neural networks (CNNs), have demonstrated remarkable success in automating the diagnosis process by learning complex patterns and features from large datasets. The paper then explores a wide range of applications of image processing in healthcare. These applications include computer-aided diagnosis, image-guided surgery, image-based treatment planning, virtual reality visualization, and medical research. Image processing techniques enable accurate detection, characterization, and quantification of diseases, facilitating early diagnosis and personalized treatment strategies. Overall, image processing techniques have revolutionized modern healthcare by enabling more accurate and efficient analysis of medical images. They play a pivotal role in various medical domains, including radiology, pathology, cardiology, and oncology. As technology continues to advance, image processing is expected to further enhance its contributions to healthcare, paving the way for more precise diagnostics, targeted therapies, and improved patient outcomes