Graph Neural Networks have emerged as powerful tools for analyzing graph-structured data. However, their performance often varies across datasets due to challenges such as noisy edges, sparse connectivity, and over-smoothing in deep layers. To address these limitations, Graph Deformation Network Convolution (GDNConv) is proposed as a novel graph convolution model that incorporates four key innovations: dynamic edge weight learning to filter noisy connections, graph attention deformation to prioritize relevant neighbors, multi-level aggregation to capture multi-scale patterns, and self-regularization to stabilize training. This proposed model demonstrates robustness and scalability, particularly for real-world applications involving complex and noisy graph structures, such as social networks and recommendation systems. It has the ability to dynamically adapt graph topology during training and superior performance on both dense and sparse datasets highlight its potential as a versatile solution for graph-based learning tasks. Additionally, GDNConv’s computational efficiency and self-regularization mechanisms make it suitable for large-scale applications where resource constraints are a concern. The proposed model is evaluated on four benchmark datasets—Cora, CiteSeer, PubMed, and ogbn-arxiv—and compared with several state-of-the-art models, including Graph Convolutional Network, Graph Attention Network, and Graph Sample and Aggregate. The experimental results demonstrate that the proposed model consistently outperforms these baseline approaches, achieving improvements of 4.7% and 4.2% in both accuracy and F1 Score.