The Surface Waves Investigation and Monitoring (SWIM) instrument onboard the China–France Oceanography Satellite (CFOSAT) can provide wave spectra using its off-nadir beams. Although SWIM shows a reasonable performance for capturing spectral peak, the accuracy of mean wave periods (MWPs) computed directly from the SWIM spectra is not satisfying due to the high noise level of the spectra. SWIM can also provide good-quality simultaneous wind speed (U10) and significant wave height (SWH) like an altimeter. The MWP can also be estimated using a U10-SWH look-up table presented in previous studies. However, the accuracy of this method is also limited as the U10-SWH look-up table is only applicable for wind-sea-dominated conditions. The two MWP retrieval methods are independent of each other, and their error properties are complementary to each other. Therefore, this study further presents a merged MWP retrieval model combining the nadir U10-SWH and the MWP from the off-nadir spectrum of SWIM using a simple artificial neural network. After training against some buoy data, the model reaches unprecedented accuracy for MWP retrievals (RMSEs of ~0.36 s for zero up-crossing periods, ~0.41 s for mean periods, and ~0.60 s for energy periods), demonstrating the usefulness of SWIM in the studies of ocean waves.