Underwater imaging presents unique challenges compared to open-air photography, primarily due to diminished visibility and geometric distortions, impeding the development of underwater Computer Vision (CV) and robotic vision perception. Previous methods relying on simplified image formation models for image enhancement have often yielded unsatisfactory results. This paper proposes a new deep learning-based architecture for joint depth estimation and dehazing from a single underwater monocular image, seeking to take advantage of the mutual benefits between these two interrelated tasks. The proposed architecture is a Two-Headed Depth Estimation and Dehazing Attention Network (2HDED:AttN) with an end-to-end training approach. Comprehensive experiments on synthetic and real underwater datasets showcase the proposed architecture’s superior performance in jointly addressing underwater depth estimation and image dehazing tasks. The method effectively estimates underwater depth and improves underwater image quality, paving the way for enhanced underwater computer and robotic vision applications.