Background:Modern sequencing technologies have enabled the reconstruction of complete mammalian genomes from telomere to telomere. However, scaling this achievement to thousands of species and population-level studies remains a challenge. Key bottlenecks include the low quality of the draft assemblies and the high coverage requirements. In particular, reconstructing complete and accurate sequences of both haplotypes in diploid genomes is especially difficult since the sequencing depth is not always sufficient to properly reconstruct diverged regions. We aim to explore the use of machine learning, specifically graph neural networks, for scalable error correction in De Bruijn Graphs, addressing the limitations of existing heuristic methods in genome assembly. Results: Inspired by the success of neural networks in extracting patterns from the data on a massive scale, we introduce a method for correcting errors in De Bruijn Graphs using Graph Neural Networks. Our model provides a reliable classification of edges into correct and erroneous, especially for diploid genomes with coverage depth 35 and lower. We demonstrate that these predictions can guide the downstream read error correction algorithm and genome assembly, ultimately allowing for more accurate genome assembly. Conclusions: Machine learning methods have the potential to replace heuristic methods commonly used in genome assembly. Learning-based approaches can enhance the performance of existing assemblers in challenging scenarios and facilitate adaptation to newly sequenced species.