This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

Microalgae have emerged as a promising source of renewable energy and natural bioproducts since they show high biomass productivity, offer carbon dioxide fixation, and exhibit a rich content of compounds. Recent efforts have focused on green extraction technologies that utilize green solvents to further promote sustainability and minimize the environmental impact of the microalgal process. At this point, life cycle analysis (LCA) provides valuable insights into the environmental impacts of specific products and techniques. A comprehensive overview of the life cycle environmental and energy assessments conducted for the extraction of metabolites from microalgae is presented. Special attention is given to using green extraction technologies, i.e., supercritical fluid extraction, pressurized liquid extraction, microwave‐assisted extraction, ultrasound‐assisted extraction, and pulsed‐electric field extraction, and solvents to ensure sustainability. Additionally, the main principles, historical development, tools, and challenges of LCA are discussed. By addressing these aspects, the paper attracts attention to the environmental impacts associated with green extraction techniques for obtaining microalgal metabolites.