Image denoising is a core problem in digital image processing and plays a vital role in applications including medical imaging, remote sensing, video surveillance, and autonomous systems. While numerous denoising techniques have been developed, most are designed for specific noise models such as Gaussian, impulse, or speckle noise, resulting in significant performance degradation under mixed and high-density noise conditions commonly encountered in real-world imaging scenarios. To address these limitations, this paper proposes MLMCD (Multi-Level Multi-Channel Denoising), a hierarchical filtering framework that integrates multi-scale spatial decomposition with cross-channel information fusion. The proposed multi-level architecture progressively refines noise estimates across resolution stages, enabling effective coarse-level noise suppression while preserving fine structural details at higher resolutions. In addition, a dedicated multi-channel fusion module exploits inter-channel correlations in colour images to achieve coherent noise attenuation across RGB channels without compromising chromatic fidelity. An adaptive noise estimation mechanism dynamically regulates filtering strength based on locally computed noise statistics, removing the need for explicit noise-type specification and enhancing robustness to previously unseen mixed-noise conditions.