In recent years, the rapid development of artificial intelligence technology has provided technical support for constructing a comprehensive biodiversity monitoring system. Expanding the biodiversity monitoring system from an acoustic perspective has become a new development trend. In soundscape ecology, soundscape comprises biophony, anthrophony, and geophony. The acoustic niche hypothesis and acoustic adaptation hypothesis provide a main theoretical foundation for assessing biodiversity using the soundscape evaluation method. This article summarizes the manual identification, acoustic indices, and automated recognition methods commonly used in biodiversity assessment. By sorting out these methods’ concepts, assessment principles, differences, and limitations, we can better comprehend the scope of their application and rules. In addition, this paper summarizes the factors that influence bias in biodiversity assessment by the soundscape, including biological factors, environmental factors, noise disturbance, and data collection methods. Species’ genetic, behavior, and environmental factors can lead to more complex species calls. The complexity of species calls increases the risk of incorrect identification and classification. Therefore, there are limitations in relying only on acoustic characteristics to evaluate biodiversity. To address the concerns raised above, it had been critical to conduct an in-depth examination of the mechanisms underlying the acoustic niche hypothesis and the acoustic adaptation hypothesis. Additionally, it has been essential to establish a more comprehensive theoretical framework and application model, including various expertise and state-of-the-art technologies. Simultaneously, it is necessary to develop standardized protocols for data collection and processing. These protocols could efficiently format data collection procedures, reducing the potential biases created by researchers during the investigation. Moreover, standardized protocols play a vital role in ensuring the reliability and repeatability of data collection procedures. In the future, we can optimize rapid biodiversity assessment based on soundscape data by fusing and innovating methods. Thereby, there is an urgent need to build a data-sharing platform on soundscape characterization. This open platform can preserve soundscape resources, alleviate the challenges of insufficient training datasets, and provide essential data for biodiversity-related research and conservation initiatives. In general, standardized protocols, advanced methods, and open soundscape platforms will provide a more efficient pathway for rapid biodiversity assessment and biodiversity protection.