<p>Medical image watermarking plays a vital role in modern healthcare by ensuring image authenticity, protecting intellectual property, preserving data integrity, maintaining patient confidentiality, and detecting unauthorized alterations. This survey provides a comprehensive review of medical image watermarking techniques, focusing on spatial, frequency, and transform-domain approaches. It summarizes their principles, strengths, limitations, and applications in real-world healthcare environments such as telemedicine, electronic health records, and clinical data management. The survey further examines commonly used performance metrics and discusses major challenges, including robustness, imperceptibility, security, embedding capacity, computational complexity, and vulnerability to attacks. Recent advancements, including deep learning-based watermarking, hybrid models, reversible techniquess, and privacy-preserving frameworks, are also highlighted. By consolidating current developments and identifying existing research gaps, this study offers insights into emerging trends and future directions for enhancing the reliability, security, and effectiveness of medical image watermarking systems.</p>