5G-Beyond (5G-B)/6G require higher spectral efficiency (SE) and data throughput (DT), motivating reduced pilot overhead in orthogonal frequency-division multiplexing (OFDM) systems. Traditional methods target temporal prediction and address frequency selectivity via pilot-tone interpolation, leaving time–frequency prediction unexplored. To address this gap, we propose a Transformer-based pilot-to-prediction (P2P) neural network (NN) for joint channel estimation and prediction. Exploiting the Transformer’s self-attention, our method captures temporal dynamics and inter-subcarrier correlations to predict the frequency-selective channel across all subcarriers using only past pilot observations, requiring no pilots in the symbols being predicted. Simulation results show that our approach closely matches the accuracy of a genie-aided receiver with perfect channel knowledge and outperforms Wiener filtering and long short-term memory (LSTM) baselines. Consequently, pilot overhead is substantially reduced, enhancing SE by 33.3%. Achievable data rates are thus improved, underscoring the suitability of Transformer-based channel prediction for future wireless standards.