<p>The rapid urbanization and industrial growth have led to a significant increase in municipal wastewater and residual activated sludge production. Globally, the activated sludge process treats approximately 80–90% of municipal wastewater in developed countries and over 60% in China, making sludge from these systems the majority of total sludge requiring treatment. This review critically examines the challenges associated with anaerobic digestion of complex sludge matrices, including protective extracellular polymeric substances, rigid microbial cell walls, and emerging contaminants such as antibiotics and antibiotic resistance genes. Traditional enhancement methods—physical, chemical, and biological pre-treatment strategies—have demonstrated potential in increasing soluble organic matter and improving methane production. However, these methods alone cannot consistently overcome inherent sludge variability. The novelty of this review lies in its systematic exploration of how recent advances in intelligent control technologies—featuring real-time monitoring, adaptive automatic control, deep learning, and predictive analytics—can be synergistically integrated with conventional pre-treatment to dynamically optimize anaerobic digestion. Key findings indicate that such integration enables superior process stability, enhanced organic degradation, and increased biogas yields through adaptive responses to fluctuating sludge characteristics. We conclude that the convergence of intelligent control with conventional methods offers a transformative pathway for sludge management. Recommendations include prioritizing sensor reliability, investing in data integration infrastructure, and conducting pilot-scale demonstrations to validate economic feasibility and system scalability.</p> Graphical Abstract <p></p>