The popularity of networks and the ongoing advancement of multimedia technology have led to an increase in the transmission of digital images through insecure channels, the need to conserve network bandwidth, and the steady increase in public awareness of copyright protection for multimedia information. Digital watermarking gives a powerful method for identifying model ownership and provides a defense against such dangers, which implies the creation of numerous eminent watermarking strategies by possible researchers. This paper reviews available techniques on image watermarking for implementation and its growth in using machine learning models. This research proposed a taxonomy for categorizing and analyzing several classes of watermarking techniques for machine learning models. Finally, thorough comparisons are made between machine learning-based watermarking techniques that offer robustness, interpretability, and embedding solid capability. Researchers can thus understand an effective machine learning model for applications with the guidance of this review paper.