Abstract: Machine learning (ML) methods, with their excellent data parsing and pattern recognition capabilities, have shown significant application potential in the field of organic solid waste (OSW) treatment. With the increasing demand for OSW treatment and the advancement of technological innovation, the application of ML in this field is rapidly becoming popular. This study systematically explores the application of ML methods in the treatment and resource recovery of OSW, highlighting the potential of ML to address the heterogeneity and complexity inherent in OSW. The research initially defines the scope of OSW, demonstrated the limitations of traditional treatment technologies when confronted with the diverse origins and heterogeneous compositions of OSW. Through an exhaustive review of academic literature from 2018 to 2023, this study identifies research trends and hotspots in the application of ML to OSW treatment. It particularly focuses on the potential of four commonly used ML models—Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and Genetic Algorithms (GA)—in enhancing the efficiency and resource recovery rate of OSW treatment. The strengths, weaknesses, and applicability of these models across various stages of OSW management, including source reduction, classification, thermochemical conversion, anaerobic digestion, and aerobic composting were discussed. The findings indicate that ML techniques significantly improve the predictive accuracy and process optimization capabilities in OSW treatment. Notably, ML models have demonstrated remarkable advantages in predicting waste characteristics and simulating biological treatment processes. However, key challenges remain, including data quality, model generalization ability, and algorithm selection. To address these challenges, the study proposes a series of strategies, such as the development of integrated models and the integration of interdisciplinary technologies. These strategies aim to provide scientific guidance and technical support for the resourceful utilization of OSW. The study concludes by emphasizing the importance of selecting the appropriate ML model based on data structure and size, as well as the specific nature of the OSW treatment process. It also suggests that further research should focus on real-time process monitoring and control using ML technologies to enhance product yield in process systems and explore the potential of algorithms combination or combining trained ML models with other data-driven control strategies for comprehensive and automated process optimization.