Conventional semisolid magnetization transfer contrast (MTC) and chemical exchange saturation transfer (CEST) MRI studies typically employ acquisition of a series of images at multiple RF saturation frequencies. Furthermore, quantitative MTC and CEST imaging techniques based on MR fingerprinting (MRF) require a range of values for multiple RF saturation parameters (e.g. B1, saturation time). These multiple saturation acquisitions lead to a long scan time, which is likely vulnerable to motion during in vivo imaging. Motion correction is hard due to varying image intensity between acquisitions. Herein, we proposed a deep learning-based motion correction technique for conventional Z-spectra and MRF data.