Digital pre-distortion (DPD) is a well-established and highly effective technique for linearizing radio frequency power amplifiers. With the advent of 5.5G/6G base stations and Wi-Fi 7, DPD has become increasingly critical to support wideband signals and higher data rates. State-of-the-art DPD models predominantly leverage neural networks to process time-domain (TD) signals. While the TD-DPD enables point-to-point pre-distortion of TD signals, frequency-domain (FD) DPD requires the entire spectrum as input to generate time-domain outputs. This requirement results in a large number of input neurons and significantly increases computational complexity, thereby limiting the practical application of FD-DPD and hindering insights into frequency-dependent distortion characteristics. This paper introduces a lightweight frequency-domain (FD) neural network architecture incorporating a novel feature extraction method and integrate it with a TD-DPD model to develop an innovative Hybrid-DPD model. Evaluated on the OpenDPD platform with DPA_200MHz dataset, the Hybrid-DPD achieves simulated ACPR of -50.28 dBc, EVM of -45.30 dB and NMSE of -42.92 dB with 578 parameters. This performance surpasses that of the time-domain-only DPD with a comparable parameter count of486 by approximately 1 dB.