With the increasing of array antennas and the growing complexity of anti-jamming， traditional adaptive beamforming methods often suffer from high computational complexity. Deep learning， with its powerful data-driven capabilities， offers a novel approach to overcoming the performance bottlenecks of traditional adaptive beamforming. This paper provides a systematic review on current studies and development trends of deep learning in array antenna beamforming. First， we revisit the evolution of traditional beamforming algorithms，ranging from the Howells-Applebaum adaptive processor to robust beamforming based on convex optimization. Second， we analyze the innovative applications of deep learning models such as convolutional neural networks （CNNs）， recurrent neural networks （RNNs）， and long short-term memory （LSTM） networks in beamforming. This review demonstrates that deep learning methods exhibit significant advantages in improving system performance due to their powerful nonlinear modeling capabilities， end-to-end optimization characteristics， and environmental adaptability. Specifically， in mobile communications， deep learning-based beamforming methods substantially enhance the computational efficiency and environmental adaptability of massive multiple input multiple output （MIMO） systems. In radar signal processing， deep learning models effectively improve anti-jamming performance and target detection accuracy. In acoustic signal processing， deep neural networks enable more precise sound source localization and noise suppression. Finally，this paper explores future research directions， including network architecture innovation， real-time processing optimization， robustness enhancement， cross-scenario transfer learning， theoretical foundation deepening， and novel application expansion.