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Drug combination therapies can improve cancer treatment efficacy while overcoming drug resistance. However, with the increasing number of available drugs, predicting drug synergy within the combination space remains a challenge. For instance, current approaches often struggle with ineffective drug representations and the challenge of effectively incorporating cell line information. To address these limitations, we propose PMDSynergy, a novel drug synergy prediction framework that integrates pre-trained drug embeddings and cell line features by multi-dimensional data fusion. Leveraging the strength of pre-trained encoding techniques in terms of representational power, this method integrates drug features across three dimensions and seven omics data features, which are then integrated through a multi-modal fusion strategy based on contrastive learning to enhance the model's predictive ability. Our approach demonstrates superior performance compared to existing methods. Furthermore, we validate the robustness of our model by assessing its performance on previously unseen drug combinations, confirming its generalization capability. Overall, PMDSynergy innovatively utilizes multi-dimensional pre-trained embeddings to more accurately predict drug synergy through comprehensive representations, providing a reliable and efficient framework for drug synergy discovery. Availability: The code of our work is available at https://github.com/JieZheng-ShanghaiTech/PMDSynergy.