The generation of waste activated sludge (WAS) in China has reached substantial levels, posing significant challenges in both disposal costs and environmental management. As urbanization and industrialization continue to accelerate, the effective treatment and disposal of WAS have become a pressing issue that necessitates efficient and sustainable solutions. The importance of utilizing WAS resources is further underscored in the context of rising energy demand and the "Dual Carbon Goals", which aim to reduce carbon emissions and achieve carbon neutrality. In this context, anaerobic fermentation has emerged as a promising "waste-to-energy" technology, offering the potential to recover valuable resources from WAS. Among these resources, short-chain fatty acids (SCFAs) are among the most notable products of anaerobic fermentation. SCFAs have high commercial value and diverse applications in industries such as bioplastics, food, pharmaceuticals, cosmetics, leather processing, and biofuels. However, the anaerobic fermentation of WAS faces several inherent challenges, including low hydrolysis efficiency and poor biodegradability, which severely limit SCFA production. To address these challenges, researchers have explored various pretreatment methods, including physical, chemical, biological, and combined approaches, to enhance the fermentation process and improve SCFA yields. While existing studies primarily focus on the efficiency of sludge acidogenic fermentation, a comprehensive analysis of the underlying mechanisms and optimal strategies for maximizing SCFA production is still lacking. This study aims to fill this gap by systematically analyzing the efficiency and mechanisms involved in enhanced SCFA production from WAS using different pretreatment techniques. It critically evaluates the advantages and disadvantages of each pretreatment method, examining their respective impacts on the fermentation process. Additionally, key operational parameters, such as pH, temperature, solid retention time (SRT), organic loading rate (OLR), and reactor types, are investigated to optimize SCFA yields. Furthermore, the environmental and economic implications of implementing these pretreatment strategies in large-scale sludge management are analyzed. Finally, this study suggests that developing green and efficient pretreatment methods, integrated with artificial intelligence, represents a promising direction for future research. The findings of this research are expected to provide valuable insights for improving sludge resource management efficiency, contributing to both sustainable waste management and the advancement of circular economy practices.