Enhancing underwater images is a challenging task due to poor visibility, low contrast, and color distortion caused by light absorption and scattering. This paper presents a novel image enhancement approach based on a fractional-order integro-differential equation (IDE) framework. The proposed model combines fractional differentiation for edge and texture preservation with integral operators for effective noise suppression. Additionally, a deep learning-based pre-enhancement step is incorporated to improve overall image clarity. The method is evaluated on a benchmark dataset using standard quality metrics, such as the Underwater Image Quality Measure and the Underwater Color Image Quality Evaluation. Experimental results show that the proposed hybrid IDE-based approach outperforms conventional method, delivering significant improvements both visually and quantitatively.