In addressing the challenges posed by edge information loss and inadequate multi-scale feature integration in remote sensing image change detection, this paper proposes a novel approach: the gradient image-guided multi-scale feature fusion network (GGMNet). The primary innovations comprise a gradient-guided module (GRAD) and a multi-scale depthwise fusion module (MSDF). The GRAD module is responsible for generating gradient maps through the gradient variation magnitude of bi-temporal images (captured at two different time points for the same location), enhancing edge features using linear self-attention, and suppressing pseudo-change noise through spatial-channel attention. The MSDF integrates features at various levels to explore hierarchical semantic information, ranging from specifics to global context. To assess the efficacy of change detection, We use four widely adopted metrics: precision, recall, the F1 score, and intersection over union (IoU). Extensive experimentation on the LEVIR-CD and SYSU-CD datasets has been conducted to demonstrate the efficacy of the proposed method.