Lung cancer remains a leading cause of cancer-related mortality worldwide, and combination therapy is an important strategy for improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug combinations remains challenging because of the large combinatorial search space and the strong dependence of drug responses on cellular context. Here, we present SynerHyper, a multimodal computational framework for lung cancer drug synergy prediction that integrates three-dimensional molecular representations, transcriptomic profiles, hypergraph learning, and ensemble fusion. Drug molecules are encoded using a Uni-Mol-based molecular encoder, while high-dimensional cell-line gene-expression profiles are compressed using a deep autoencoder. Each drug–drug–cell-line observation is represented as a three-node hyperedge, enabling a two-layer hypergraph neural network to model higher-order interactions among the two drugs and their cellular context. Data augmentation is applied exclusively to the training subset to increase training-data diversity and mitigate class imbalance, and predictions from the hypergraph model, multilayer perceptron, and LightGBM are combined through soft ensemble fusion. Under the split-before-augmentation evaluation protocol, the validation set was used for model selection, while the test set remained completely isolated until final evaluation. SynerHyper achieved a validation AUC of 0.815 and an independent test AUC of 0.808, outperforming the strongest baseline DeepDDS (GCN, 0.792) and other representative methods including AttenSyn (0.780), DeepSynergy (0.750), and DTSyn (0.690) under the same lung cancer dataset. Together with an accessible online prediction server (SynerHyper), SynerHyper provides a lung cancer-specific computational framework for prioritizing candidate drug combinations for downstream experimental validation.