Side-Scan Sonar (SSS) is widely used in seabed mapping, underwater exploration, and object detection due to its high-resolution acoustic imaging. However, interpreting SSS images is challenging due to poor contrast, noise, and lack of depth cues. This study proposes an enhancement method based on RGB-D conversion, integrating acoustic intensity and estimated depth to improve image clarity and object perception. The approach involves preprocessing raw sonar data, estimating depth, and converting to RGB-D format. The RGB-D representation enables 3D point cloud generation, facilitating detailed 3D modeling of seabed features. The method supports advanced analysis techniques like segmentation and classification and enhances spatial understanding for applications such as habitat mapping and offshore construction. Evaluated on real-world datasets, the approach achieves improved signal-to-noise ratio (SNR) and reconstruction accuracy, with AbsRel and RMSE values of 0.2546 and 0.4586 respectively. This method advances SSS-based mapping by enhancing image fidelity and 3D interpretability.