The valorization of organic solid waste (OSW) has become a crucial direction for global environmental governance and energy structure optimization, as it addresses both waste pollution and energy shortage challenges. Anaerobic digestion (AD) is a core biotechnological process that converts organic matter in OSW into methane-rich biogas, thereby achieving the dual objectives of energy recovery and waste volume reduction. However, practical AD systems often suffer from insufficient stability, over-reliance on empirical parameter tuning, and poor adaptability to fluctuating operational conditions, which significantly hinder their large-scale implementation and efficiency. Artificial intelligence (AI), owing to its superior capabilities in nonlinear modeling, time-series forecasting, and multi-variable optimization, offers a promising technical pathway to overcome these challenges. AI can support various aspects of the AD process, including process modeling, operational parameter optimization, fault detection and warning sytems, pretreatment strategy enhancement, and microbial community regulation, by offering high prediction accuracy and rapid response. This review systematically summarizes cutting-edge AI applications in AD, focusing on advancements in deep learning, automated machine learning (AutoML), and reinforcement learning, and thoroughly analyzes the feasibility and value of deep integration between AI and AD technologies. Considering the significant compositional heterogeneity of various OSW types (e.g., food waste, sewage sludge, and livestock manure), these substrates display substantial variations in biodegradability and methane potential, leading to fluctuating kinetic behavior in AD systems. To address this, AI models have demonstrated improved generalization through rigorous feature selection and have been widely applied across critical stages of the AD process. Recent studies indicate that ensemble learning models such as Random Forest and XGBoost can achieve coefficients of determination (R²) exceeding 0.95 for predicting methane yields in multi-substrate AD systems. In operational optimization, AI-enabled intelligent control systems have shown potential in optimizing feeding strategies and adjusting key process parameters (e.g., temperature, pH, hydraulic retention time), resulting in biogas yield improvements of 43% to 45% compared to conventional approaches. Additionally, AI plays a pivotal role in fault diagnosis, substrate ratio and pretreatment optimization, and microbial community regulation, thereby further demonstrating its broad applicability in AD systems. Despite these advances, AI-driven AD technologies still face key challenges, including inconsistent data quality from complex substrates, limited real-time monitoring infrastructure, low model interpretability, poor cross-system generalizability, and the absence of standardized engineering frameworks. Future research should prioritize the development of interpretable, low-data-demand hybrid models and the establishment of intelligent, real-time monitoring and closed-loop control systems based on the Internet of Things (IoT) and digital twin technologies. Such efforts will facilitate the large-scale, intelligent, and low-carbon development of AD for OSW, thus supporting carbon neutrality goals and sustainable resource recovery.