In the treatment of complex diseases, single drug therapy is limited by resistance and tolerance, making the exploration of efficient drug combinations crucial in cancer research. When evaluating drug efficacy, traditional deep learning methods analyze single-drug sensitivity and drug combination synergy in isolation, which cannot capture the complex internal relationship between them, resulting in prediction deviation. To overcome these limitations, this article proposes a novel drug combination synergy prediction model called MTDSN (Multi-Task Deep Synergy Network), which integrates multi-task learning and deep neural networks to simultaneously predict single-drug sensitivity and drug combination synergy. During the model construction, the Autoencoder integrated with Convolutional Block Attention Module (CBAM) is used to reduce the dimension of input features, and then the drug features and cell line features are connected and input into the shared module embedded with cross-stitch mechanism to exchange information. Finally, the features of each task are input into different task-specific branches to obtain the synergy score of the drug combination, the sensitivity score of the single drug and their corresponding classification results. Evaluated on the O’Neil dataset, MTDSN achieves the lowest mean squared error (MSE) and highest Pearson correlation coefficient (PCC) in drug synergy prediction, with an ROC-AUC of 0.92 and accuracy of 0.95 in the classification task, demonstrating substantial improvements in predictive efficacy.