  In the realm of 5G communication systems, the accuracy of Channel State
Information (CSI) prediction is vital for optimizing performance. This letter
introduces a pioneering approach: the Spectral-Temporal Graph Neural Network
(STEM GNN), which fuses spatial relationships and temporal dynamics of the
wireless channel using the Graph Fourier Transform. We compare the STEM GNN
approach with conventional Recurrent Neural Network (RNN) and Long Short-Term
Memory (LSTM) models for CSI prediction. Our findings reveal a significant
enhancement in overall communication system performance through STEM GNNs. For
instance, in one scenario, STEM GNN achieves a sum rate of 5.009 bps/Hz which
is $11.9\%$ higher than that of LSTM and $35\%$ higher than that of RNN. The
spectral-temporal analysis capabilities of STEM GNNs capture intricate patterns
often overlooked by traditional models, offering improvements in beamforming,
interference mitigation, and ultra-reliable low-latency communication (URLLC).
