Understanding the change in the habitat distributions and abundance of wildlife in space and time is critical for the conservation of biodiversity and mitigate human–wildlife conflicts (HWCs). Tibetan antelope or chiru (Pantholops hodgsonii), Tibetan gazelle or goa (Procapra picticaudata), Tibetan wild ass or kiang (Equus kiang), and Wild yak (Bos mutus) have been sympatric on the Qinghai–Tibetan plateau (QTP) for numerous generations. However, reviews on the habitat distributions and abundance of these four wild herbivores (WHs), as well as the methods examining the changes in these aspects, are still lacking. Here, we firstly review the distributions and abundance of four major WHs on the QTP across different periods, examining the underlying causes of changes and HWCs. Furthermore, we critically compare three aspects of methods: transect surveys, machine learning (ML), and deep learning (DL) methods of studying WHs. The results show that since the 1990s, the distributions and abundance of WHs have exhibited a trend of initial decline followed by recovery, largely attributed to global climate warming and a decrease in illegal hunting. However, in recent years, the primary challenge has shifted from wildlife protection to balancing the human and wildlife interests within the constraints of limited resources. In the future, we should focus on enhancing the ecological functions of habitats to achieve harmonious coexistence between humans and nature, as well as establishing a scientific compensation mechanism to mitigate human–wildlife conflicts. In order to accurately calculate the changes, we should select appropriate models to analyze the habitats of wildlife based on their specific characteristics and the environmental conditions. Additionally, with the advancement of large models, AI (artificial intelligence) should be utilized for precise and rapid wildlife conservation. The findings of this study also provide guidance and reference for addressing the issues related to wildlife habitats and abundance in other regions globally.