Abstract: Audio technology is characterized by the high qualities of speed, accuracy, cost-effectiveness, non-contact, and non-invasiveness. Therefore, the key technology has been widely used in livestock breeding, and the cultivation of fruits and vegetables to drive the digitization and intelligence of agriculture. In this study, a comprehensive overview was presented on three audio technologies: audio enhancement, audio recognition, and audio control, in the livestock, fruits, and plants. Firstly, traditional filtering, short-time spectral estimation, and wavelet denoising were employed in audio enhancement. Standardized techniques were just simply applied without considering outside noises, leading to the serious extraction of pure audio. Thus, it was necessary to focus on the audio enhancement of livestock in follow-up studies. Secondly, the audio recognition was reviewed on the non-destructive testing of agricultural products, animal disease monitoring, species identification, and pest detection. A target detection model was also constructed using audio features, according to the differences between animal spontaneous vocalizations and plant excited vocalizations. It was noted that sound recognition was dominated to enhance the recognition models in current research. However, it was still lacking in the theoretical investigation into the underlying processes of both spontaneous and excited vocalization in plants and animals. Moreover, the denoising techniques were either overly simplistic or entirely absent in the pre-processing stage of audio recognition. The stability and accuracy of audio recognition were required to consider the external environment. Thirdly, the audio control was examined in fruit and vegetable cultivation, as well as livestock breeding. The existing studies were predominantly focused on the influence of audio or music on specific states of livestock, fruits, and plants. It was highly demanded to determine the dynamic changes in these states over time, particularly in response to environmental variations. Finally, the future audio technology was outlined in the context of livestock, fruits, and plants: 1) Audio enhancement can be expected with neural network and multi-channel separation, in order to provide the high-quality audio for audio recognition without external noise interference; 2) The underlying mechanisms can be clarified on both spontaneous and excited vocalizations in plants and animals. Specifically, the theoretical foundation can be offered to construct spontaneous audio recognition models in animals. Technical support can be used to design the plant excitation devices; 3) The governing mechanisms can be delved to clarify the dynamic impact of audio on livestock, fruits, and plants. The findings can greatly contribute to the real-time dynamic control of the growth and physiological state of livestock, fruits, and plants.