Anaerobic digestion (AD) is a cost-effective and eco-friendly method for treating solid organic wastes to produce high-value products such as biogas, volatile fatty acids (VFAs), alcohols, biohydrogen, and biofertilizers, thus meeting the goals of sustainable development. Knowledge of feedstock pretreatment, modeling, operation, and optimization of the AD process is essential for successful management and reaping considerable economic and environmental benefits. The conventional AD optimization and control methods are now being replaced by advanced computational techniques to predict AD performance. In recent years, there has been significant focus on machine learning (ML) for optimizing AD processes, predicting unknown parameters, detecting perturbations, and monitoring in real-time. This chapter offers an in-depth exploration of high solids anaerobic digestion (HSAD), which conserves water, enhances the organic loading rate (OLR), minimizes nutrient loss in the digestate, and eliminates the requirement for dewatering. It also discusses in detail the pretreatment strategies and the critical factors that influence the process of AD. The chapter emphasizes the types of high solid anaerobic digesters, their operation and control, and the production of value-added products like biogas, biohydrogen, bioethanol, lipids, and fatty acids. This chapter critically examines the applications of ML in AD and provides an assessment of essential algorithms. It provides an understanding of high throughput technologies to study microbial community dynamics of the anaerobic digestion process.