Microalgae offer significant potential in diverse fields, including biofuels, carbon capture, and high-value bioproducts. However, optimizing and scaling microalgae cultivation systems present several challenges due to the dynamic interactions among environmental factors such as light intensity, temperature, pH, nutrient concentration, and CO₂ levels, as well as species-specific biological variability. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and automation, have provided innovative solutions to these challenges. This review explored the role of AI in enhancing microalgae technology, focusing on optimizing cultivation conditions, improving CO₂ capture, maximizing biomass production, and automating system processes. Key case studies highlight successful applications of AI in biofuel production, carbon capture projects, and high-value compound manufacturing. Key case studies demonstrate that AI-driven models can increase biomass productivity by up to 15–57%, improve CO₂ biofixation efficiency, and enhance lipid and high-value compound yields by more than 20–43% compared to traditional methods. Additionally, we discussed the limitations of current AI models, particularly in data availability and species-specific variability, and suggested future research directions to enhance the integration of AI and microalgae systems. By leveraging AI’s potential, microalgae technologies can become more efficient, scalable, and economically viable, addressing global sustainability challenges such as energy production and climate change mitigation.

Microalgae are considered promising sustainable feedstocks for the production of food, food additives, feeds, chemicals and various high-value products. Marine microalgae Phaeodactylum tricornutum, Isochrysis galbana and Nitzschia laevis are rich in fucoxanthin, which is effective for weight loss and metabolic diseases. The selection of microalgae species with outstanding nutritional profiles is fundamental for novel foods development, and the nutritional value of P. tricornutum, I. galbana and N. laevis are not yet fully understood. Hence, this study investigates and analyzes the nutritional components of the microalgae by chromatography and mass spectrometry, to explore their nutritional and industrial application potential. The results indicate that the three microalgae possess high nutritional value. Among them, P. tricornutum shows significantly higher levels of proteins (43.29%) and amino acids, while I. galbana has the highest content of carbohydrates (25.40%) and lipids (10.95%). Notwithstanding that P. tricornutum and I. galbana have higher fucoxanthin contents, N. laevis achieves the highest fucoxanthin productivity (6.21 mg/L/day) and polyunsaturated fatty acids (PUFAs) productivity (26.13 mg/L/day) because of the competitive cell density (2.89 g/L) and the advantageous specific growth rate (0.42/day). Thus, compared with P. tricornutum and I. galbana, N. laevis is a more promising candidate for co-production of fucoxanthin and PUFAs.