Machine learning models for predicting IR spectra of molecular ions (infrared ion spectroscopy, IRIS) have yet to be reported owing to the relatively sparse experimental data sets available. To overcome this limitation, we employ the Graphormer-IR model for neutral molecules as a knowledgeable starting point and then employ transfer learning to refine the model to predict the spectra of gaseous ions. A library of 10,336 computed spectra and a small data set of 312 experimental IRIS spectra is used for model fine-tuning. Nonspecific global graph encodings that describe the molecular charge state (