The advent of deep learning has brought a revolutionary transformation to image denoising techniques. The persistent challenge of acquiring noisy-clean pairs for supervised methods in real-world scenarios remains formidable, necessitating the exploration of more practical self-supervised image denoising. However, although a large number of reviews on deep learning image denoising methods have emerged in recent years, there is no review specifically on self-supervised image denoising methods. This paper focuses on self-supervised image denoising methods that offer effective solutions to address this challenge. Our comprehensive review analyzes the latest advancements in self-supervised image denoising approaches, categorizing them into three distinct classes: General methods, Blind Spot Network (BSN)-based methods, and Transformer-based methods. For each class, we provide a concise theoretical analysis along with their practical applications. To assess the effectiveness of these methods, we present both quantitative and qualitative experimental results on various datasets. Additionally, we critically discuss the current limitations of these methods and propose promising directions for future research. By offering a detailed overview of recent developments in self-supervised image denoising, this review serves as an invaluable resource for researchers and practitioners in the field, facilitating a deeper understanding of this emerging domain and inspiring further advancements.