Machine learning (ML), a subset of AI, is transforming industries by enabling tasks such as decision-making and data analysis. In communications, AI and ML are advancing digital predistortion (DPD), a technique critical for reducing signal distortion and improving power-amplifier (PA) efficiency. This article introduces an artificial neural-network-based DPD framework that leverages PA data to reduce gain/phase errors, enhance efficiency, and improve spectral performance, surpassing traditional methods. Enhancing PA Efficiency: Digital Predistortion Meets AI InnovationDigital predistortion is a critical technique enabling power amplifiers to operate efficiently near the saturation region without compromising linearity. Consequently, DPD improves signal quality while enabling the PA to operate at peak efficiency.