Image denoising is a substantial task in image processing, especially for high-fidelity applications such as remote sensing, medical imaging, surveillance, and security. Hybrid techniques have risen as an effective solution for real-world image denoising. Deep learning (DL) methods such as convolutional neural networks and variational autoencoders significantly enhance image denoising by learning hierarchical features and utilizing large-scale datasets; however, they are limited by high computational costs and artifact generation. Simultaneously, adaptive filters such as Wiener, Non-Local Means, and Kalman filters adjust their parameters, making them robust against varying noise levels with low computational cost. However, they are limited in their ability to generalize. To address these challenges, hybrid techniques integrating DL with adaptive filters have risen as an effective solution, leveraging the data-driven capability of neural networks and the flexibility of adaptive filters for real-world image denoising. This survey systematically analyzes DL, adaptive filtering, and hybrid methods from 2013 to 2025. After investigating 35 studies, a Hybrid Spatial-Spectral Denoising Network stands out, achieving a PSNR of 43.26 dB and an SSIM of 0.9961. Finally, this review discusses current limitations and outlines future research directions for developing a robust, generalized, and computationally efficient hybrid denoising framework for real-world applications.