The transformative role of Artificial Intelligence (AI) in enhancing the performance, sustainability, and scalability of biomass energy systems is explored here. Biomass, as a key renewable energy source, faces challenges due to the variability of feedstock, complex conversion processes, and supply chain inefficiencies. AI techniques including machine learning, neural networks, deep learning, and reinforcement learning offer powerful tools to optimize biomass operations from feedstock assessment to energy output prediction and process control. The chapter reviews current trends such as predictive modeling, smart feedstock management, real-time monitoring via integration with Internet of Things (IoT) and lifecycle assessment. Case studies highlight practical applications in anaerobic digestion, gasification, and decision support systems. Challenges such as data limitations and model interpretability are addressed, along with future directions emphasizing hybrid modeling, edge AI, and circular bio-economy integration. AI is positioned as a critical enabler for smarter, more sustainable biomass energy solutions.