Abstract:The efficient treatment and resource utilization of organic solid waste have become critical issues for advancing environmental sustainability and addressing energy demands, serving as key responses to national environmental strategies. Traditional treatment technologies face challenges such as low conversion efficiency, coarse-grained process control, and difficulties in secondary pollution control. In contrast, artificial intelligence (AI) technology is rapidly emerging as a transformative force in organic solid waste energy and environmental research. By leveraging its powerful capabilities for high-dimensional data modeling, automated pattern recognition, feature extraction from multi-modal data streams, and intelligent sequential decision-making under uncertainty, AI offers unprecedented opportunities to address these limitations. This paper systematically reviews the paradigm shifts driven by AI in research methodologies, technological processes, and management modes within the field of organic solid waste energy and environment. It provides an in-depth analysis of the integration mechanisms and distinctive characteristics of AI with core operational aspects, including (i) big data analytics and knowledge graph construction for system-wide decision support and policy simulation; (ii) intelligent regulation and real-time optimization of biological and thermochemical conversion processes through hybrid modeling and reinforcement learning; (iii) automated identification, classification, and quality assessment of complex waste streams using advanced computer vision and multi-sensor fusion; and (iv) smart management of treatment facilities encompassing predictive maintenance, fault detection and diagnosis, and human-machine collaborative operation. Through representative large-scale implementation case studies—including the Tiangong AI Environmental Large Language Model for domain-specific knowledge retrieval and decision assistance, AI-powered smart waste-to-energy plants achieving multi-objective combustion optimization, and intelligent control systems for full-scale kitchen waste anaerobic digestion and composting facilities—this paper demonstrates the practical effectiveness of specific AI technologies. Notably, machine learning, computer vision, and large language models substantially enhance system energy efficiency, minimize environmental risks through proactive emission control, and transform conventional operational models toward autonomy and intelligence. Furthermore, the paper explores future research directions for the convergence of AI with cutting-edge technologies such as the Internet of Things (IoT), blockchain, and quantum computing. Crucially, it delves into the foundational principles underpinning AI-driven intelligent decision-making and management, addresses core technical challenges including the integration of multi-modal sensing technologies, and evaluates the potential ethical concerns related to data privacy, algorithmic fairness, and accountability. The study indicates that AI, through a data- and knowledge-driven mechanism, is advancing the organic solid waste treatment field toward refinement, intelligence, and systematization. This technological evolution provides essential scientific support and decision-making guidance for the industry to achieve green, low-carbon, and sustainable development within the broader context of the circular economy and carbon neutrality goals. Close-