The generation of waste activated sludge (WAS) in China has reached substantial levels,posing significant challenges in both disposal costs and environmental management. As urbanizationEnergy Environmental Protectionand industrialization continue to accelerate, the effective treatment and disposal of WAS have become apressing issue that necessitates efficient and sustainable solutions. The importance of utilizing WASresources 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, anaerobicfermentation has emerged as a promising "waste-to-energy" technology, offering the potential torecover valuable resources from WAS. Among these resources, short-chain fatty acids (SCFAs) areamong the most notable products of anaerobic fermentation. SCFAs have high commercial value anddiverse applications in industries such as bioplastics, food, pharmaceuticals, cosmetics, leatherprocessing, and biofuels. However, the anaerobic fermentation of WAS faces several inherentchallenges, including low hydrolysis efficiency and poor biodegradability, which severely limit SCFAproduction. To address these challenges, researchers have explored various pretreatment methods,including physical, chemical, biological, and combined approaches, to enhance the fermentation processand improve SCFA yields. While existing studies primarily focus on the efficiency of sludge acidogenicfermentation, a comprehensive analysis of the underlying mechanisms and optimal strategies formaximizing SCFA production is still lacking. This study aims to fill this gap by systematicallyanalyzing the efficiency and mechanisms involved in enhanced SCFA production from WAS usingdifferent pretreatment techniques. It critically evaluates the advantages and disadvantages of eachpretreatment method, examining their respective impacts on the fermentation process. Additionally, keyoperational parameters, such as pH, temperature, solid retention time (SRT), organic loading rate(OLR), and reactor types, are investigated to optimize SCFA yields. Furthermore, the environmentaland economic implications of implementing these pretreatment strategies in large-scale sludgemanagement are analyzed. Finally, this study suggests that developing green and efficient pretreatmentmethods, integrated with artificial intelligence, represents a promising direction for future research. Thefindings of this research are expected to provide valuable insights for improving sludge resourcemanagement efficiency, contributing to both sustainable waste management and the advancement ofcircular economy practices.