Abstract:Global generation of organic solid waste (OSW) is rapidly increasing due to population growth and urbanization, posing severe environmental risks when improperly managed. Accordingly, developing sustainable and efficient resource-recovery strategies is essential to enable a circular economy. Thermal treatment technologies—primarily pyrolysis, gasification, and incineration—are key and efficient conversion routes that substantially reduce waste volume and convert heterogeneous organic feedstocks into high-value biofuels, syngas, and biochemicals. However, the intrinsic heterogeneity of OSW and the complex, nonlinear multiphase reactions involved in thermal processes limit the accuracy and applicability of traditional kinetic and statistical models. In this context, advanced artificial intelligence (AI) techniques have emerged as an area of growing interest in environmental engineering and energy research for enabling intelligent, precise, and robust resource recovery. This review systematically evaluates recent advances in AI methods applied to OSW resource recovery, with particular emphasis on applications in core thermal treatment pathways. We critically examine the performance of mainstream machine learning and deep learning algorithms—including artificial neural networks (ANNs), random forests (RFs), support vector machines (SVMs), and state-of-the-art deep learning architectures—across diverse thermal scenarios. Analyses indicate that, compared with conventional statistical models, AI-assisted approaches can improve feedstock property prediction accuracy by approximately 15% on average and can more reliably predict pyrolysis product distributions. Nevertheless, significant challenges persist in cross-scale, multi-source data fusion and in maintaining dynamic adaptability under fluctuating industrial conditions. AI also contributes to real-time optimization of operational conditions and to intelligent control of secondary pollutant emissions (e.g., nitrogen oxides and dioxins). Beyond single-reactor applications, we summarize broader AI-enabled developments, including dynamic life cycle assessment (LCA) frameworks and digital twin systems that couple multi-sensor data with AI to provide comprehensive environmental impact assessments and to support sustainable decision-making. We further identify key bottlenecks that hinder industrial-scale deployment, notably the scarcity of standardized, high-quality industrial datasets and the limited mechanistic interpretability of black-box models. Finally, we propose corresponding solutions and research directions to facilitate the deeper integration of AI with thermal treatment technologies, thereby promoting efficient, high-value, and intelligent resource recovery of OSW. Close-