Abstract: Anaerobic fermentation of agricultural waste is a key technological pathway for realizing high-value biomass utilization and zero-carbon energy supply. With the bioenergy industry moving toward large-scale and intensive development, the efficient, stable operation and intelligent regulation of fermentation systems are facing substantial challenges. These challenges are primarily attributed to the highly nonlinear, time-varying, and multiscale coupling characteristics of complex microbial metabolism and multiphase mass transfer processes, which have long rendered fermentation systems a “black box”. Traditional mechanistic models suffer from difficulties in parameter calibration and limited generalization capability, whereas purely data-driven models are constrained by large fluctuations in agricultural waste composition, small-sample datasets, and insufficient interpretability. To overcome these bottlenecks, this paper proposes and systematically elaborates a digitally and intelligently driven paradigm characterized by a hierarchical progression of “data–mechanism–control”. First, starting from agricultural waste feedstock composition, pretreatment, and the entire fermentation process, the multiscale dynamic data characteristics and nonlinear modeling challenges are analyzed. Second, the evolution and limitations of purely data-driven models are systematically reviewed. Particular emphasis is placed on the mechanism by which Physics-Informed Neural Networks (PINNs) embed prior physical laws to improve prediction accuracy under small-sample conditions. The key role of Explainable Artificial Intelligence (XAI) in decoding the implicit mapping between process parameters and hydrogen production performance is also clarified. Finally, a framework for next-generation smart bioenergy plants centered on digital twins is envisioned, and future research directions are proposed, including deep coupling between mechanisms and data, construction of standardized databases for agricultural waste, and practical deployment of “end–edge–cloud” collaborative systems. This study provides a theoretical basis and technical reference for precise modeling, process optimization, and intelligent equipment control in biochemical hydrogen production from agricultural waste and related fermentation processes. It also offers important guidance for promoting the digital and intelligent transformation of China’s biomass energy industry.