The proliferation of digital imaging technologies, alongside sophisticated image processing tools, has significantly eased the manipulation of visual content, leading to challenges in ensuring the originality of digital images. Because it is easy to execute and challenging to detect, copy-move forgery in which copy and paste a section of one image into another image has grown in popularity among other forgery kinds. This paper presents an incisive survey of the advancements in Copy-Move Forgery Detection (CMFD) methods, focusing primarily on techniques leveraging deep learning algorithms. It categorizes existing approaches based on their core methodologies and discusses their strengths, limitations, and application scenarios. Through an extensive review, this study highlights the evolution of CMFD techniques from traditional methods based on manual feature extraction to more sophisticated and automated deep learning models. The performance of these methods is evaluated based on standard datasets, offering insights into their efficacy in detecting and localizing forgeries under various conditions, including scaling, rotation, and noise addition. The paper aims to provide a solid foundation for future research in image forgery detection, encouraging the development of more robust, efficient, and universally applicable CMFD techniques.