This chapter explores the integration of artificial intelligence (AI) in biohydrogen production, a promising renewable energy technology. Biohydrogen is regarded as a potential renewable bioenergy resource. There are many processes through which it can be produced, for example, thermochemical and biological processes like pyrolysis, electrolysis, dark fermentation, and photo-fermentation. It is more economically viable when it is produced from waste materials such as waste biomass via microbial fermentation or light-driven chemical reactions. In the last decade, AI or intelligent systems have revolutionized scientific research. Prospectively, classical AI, machine learning (ML), and deep learning algorithms can be applied to optimize biohydrogen production processes. These techniques including reinforcement learning, artificial neural networks, and genetic algorithms can help optimize crucial influential parameters affecting biohydrogen production efficiency and yield. Random forest and support vector machine are two specific ML algorithms that can improve process monitoring, yield prediction, and address challenges for biohydrogen production by managing complex data, accurately predicting outcomes with improved scalability for industrial production processes. The chapter also highlights AI applications in biohydrogen production employing various AI tools like jellyfish optimizer and adaptive neuro-fuzzy inference system that optimize operational conditions in microbial electrolysis cells, enhancing hydrogen yield from wastewater. However, there are many challenges to implement AI-based systems in practice at large that include data limitations, real-world variability, scalability, and supportive technology to AI. Moreover, intelligent systems’ limited adaptability, to date proven credibility and human oversight importance were also discussed with associated ethical concerns. It also needs continuous monitoring and improvement for economically viable and sustainable production processes. Emerging technological trends in biohydrogen production focus on autonomous AI-based production systems, predictive modeling, appropriate management of supply chain, and sustainability valuation. Future AI developments aim to make biohydrogen production more cost-effective, efficient, and scalable.