Structure elucidation is crucial for identifying unknown chemical compounds, yet traditional spectroscopic analysis remains labour-intensive and challenging, particularly when applied to large-scale datasets. Although machine learning models have successfully predicted chemical structures from individual spectroscopic modalities, they typically fail to integrate multiple modalities concurrently, as expert chemists usually do. Here, we introduce a multimodal multitasking transformer model capable of accurately predicting molecular structures from integrated spectroscopic data, including Nuclear Magnetic Resonance (NMR) and Infrared (IR) spectroscopy. Trained initially on extensive simulated datasets and subsequently finetuned on experimental spectra, our model achieves top-1 prediction accuracies up to 96%. We demonstrate the model's capability to leverage synergistic information from different spectroscopic techniques and show that it performs on par with expert human chemists, significantly outperforming traditional computational methods. Our model represents a major advancement toward fully automated chemical analysis, offering substantial improvements in efficiency and accuracy for chemical research and discovery.