Microbial bioprocessing is essential for generating diverse high-value bioproducts from sustainable sources, especially in microalgal bioproducts. But the implementation of those processes is still a hurdle since the process experiences several constraints like environmental conditions to the inherent complexity of biological interactions, making consistent and optimized implementation a real challenge. Integrating artificial intelligence (AI)-driven strategies with traditional bioprocessing can mitigate these limitations, enhancing the synthesis of value-added products by improving efficiency, scalability, and sustainability. AI enables predictive modelling and real-time optimization, improving yield and resource utilization simultaneously. Machine learning (ML) refines strain selection, metabolic pathways, and bioprocess control, particularly in microalgal product development. AI-assisted automation in microalgal bioprocess systems incorporating smart sensors and lab-on-chip technology improves accuracy and reduces manual intervention. Sustainable AI applications optimize waste valorization, energy consumption, and renewable bio-based product development while minimizing environmental impact. AI-driven Quality by Design and safety regulations, including digital twin technology, facilitate predictive risk assessment and regulatory compliance. Despite these advancements, challenges in ethics, data security, and regulatory frameworks necessitate responsible AI governance. This study explores AI’s role in bioprocess engineering, advocating for intelligent automation, optimized production, and sustainability through synergistic innovation between AI and traditional biomanufacturing methodologies.