The use of microalgae to recover nitrogen and phosphorus from wastewater has garnered significant attention

positioning it as one of the most promising and sustainable strategies in modern wastewater treatment. While various photobioreactors (PBRs) configurations have been widely applied for microalgae cultivation

limited research has focused on optimizing PBR design specifically to enhance nitrogen and phosphorus removal efficiency. The high operational costs of wastewater treatment

combined with the inherent variability of microalgal growth

have prompted the search for advanced solutions that improve nitrogen and phosphorus removal while minimizing resource consumption and enabling predictive process control. Recently

the integration of PBR systems with artificial intelligence and machine learning (AI/ML) modeling has emerged as a transformative approach to enhancing nutrient removal

particularly for nitrogen and phosphorus. This study first summarizes existing PBR designs tailored for diverse applications

then outlines strategies for system enhancement through the optimization of mixing methods

construction materials

light intensity

and light source configuration. Furthermore

computational fluid dynamics (CFD) and AI/ML modeling are presented as tools to guide the structural design and operational optimization of microalgae-based nitrogen and phosphorus removal processes. Finally

future research directions and key challenges are discussed.