This study proposes a fusion method that combines product metadata, user-item interaction data and visual cues using a Graph Neural Network (GNN) architecture for image recommendations. The study focuses on using latent features from a variety of modalities, such as product information, user behavior and photographs. We proposed an Attention Fusion method that enables customizable integration of GCN representations with textual and visual item features to improve recommendation accuracy. The system employs the Multi-View Graph Convolutional Network (MGCN) for image-based recommendations and compares its performance with popular models such as Multi-Modal Graph Convolutional Networks (MMGCN) and Bootstrap Latent Representations for Multi-modal Recommendation (BM3). The tests are conducted using three datasets: Clothing, Sports and Amazon Fashion. These datasets include latent features from text, images and interaction data. The results demonstrate that the proposed model outperforms baseline methods, particularly when sparse interactions are present.