Soundscape ecology is an emerging discipline created by Pijanowski in 2011. It extensively absorbs the knowledge of landscape ecology, biogeography, psychoacoustics, bioacoustics and acoustic ecology, and regards them as its intellectual foundations. As such a comprehensive discipline, soundscape ecology studies the acoustic pattern and process of biological and abiotic sound from a landscape across variously spatial and temporal scales, revealing the relationships and interactions between human beings, nature, and sounds. With advances of sensor technology, popularization of 5G network, and development of edge computing technology, soundscape acquisition and monitoring approaches have been widely applied to disclose the insights of ecosystem, which will simultaneously produce a large amount of soundscape data and sequentially promote the appearance of series of soundscape data analysis methods. Therefore, there is an urgent need to summarize the analysis methods and application of soundscape data. Based on the research content of soundscape ecology, this paper describes the cutting—edge data analysis methods from the soundscape elements identification, biodiversity assessment to human physical and mental health evaluation. The results indicate that, with the development of analysis technologies, especially the advancement of artificial intelligence technologies, researches of soundscape ecology has presented the tendency of technicalization. To be specific, the story of data analysis technologies of soundscape ecology is experiencing a state from manual labor to machine learning, from single feature calculation to multi-dimensional feature extraction, from single-disciplinary research to multi-disciplinary conjoint analysis. It is constantly expanding the research depth and breadth of soundscape ecology. Meanwhile, these analysis technologies urgently need to be optimized and standardized, improving the adaptability of methods and the comparability of research results. Besides, the multi-disciplinary theories and methods from ecology, computer science, and psychology should be integrated, improving the data analysis technical system of soundscape ecology.