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Synthetic lethality (SL) provides a powerful foundation for targeted oncology by identifying gene pairs whose simultaneous disruption is fatal to cancer cells while sparing healthy tissue. However, mapping these interactions experimentally across the human genome is restricted by massive combinatorial scale and high costs. To address this, computational approaches specifically graph neural networks (GNNs) have become the standard for modeling these complex biological relationships. Hence, by integrating heterogeneous knowledge graph (KG) and multi-omics data, GNNs can effectively capture the topological and molecular context required for accurate SL prediction. This review systematically evaluates the computational SL landscape across three core dimensions. First, we examine how diverse GNN architectures integrate multi-layered biological data. Second, we address model interpretability, contrasting intrinsically transparent architectures with post-hoc explanation methods, as a requirement for clinical trust. Finally, we explore the emerging role of Generative AI and large language models to leverage their zero-shot reasoning and broad biomedical knowledge.