Microalgae are a promising third-generation feedstock for bioethanol production due to their rapid growth and high-carbohydrate content, although current ethanol yields remain modest. This review article analyzes how artificial intelligence can substantially enhance microalgal bioethanol production across all stages, from cultivation to bioconversion. We structured Al applications to enhance cultivation parameters such as light, CO₂, and nutrients through real-time sensor monitoring and machine-learning control, which have increased algal biomass productivity by approximately 15-50% in preliminary trials. Discuss Al-guided strain selection and metabolic engineering strategies that leverage omics data and predictive modeling to identify high- carbohydrate phenotypes, with recent work proposing up to 30-40% improvements in product yield via Al- optimized strain design. Downstream, Al-driven process enhancement in pretreatment and fermentation can improve sugar release and fermentation effectiveness, for instance, by forecasting optimal pretreatment conditions and dynamically maintaining fermentation parameters to maximize ethanol titer. A unified digital twin framework is proposed as a future paradigm in which the digital counterpart of the algal biorefinery continuously learns and optimizes the entire process, from photobioreactor to fermentation, in silico. While Al offers significant gains in efficiency and product yield, we note that data insufficiency, model generalizability and scaling issues remain challenges. Tackling these issues through interdisciplinary partnership and data-sharing will be important. Overall, Al integration can accelerate micro-algal bioethanol development, making production more versatile, productive and eco-friendly.