Vast quantities of wastewater are discharged daily from industrial, domestic, and agricultural sources, and much of this water possesses significant potential for reuse following appropriate treatment. Instead of being treated as waste, this water can be utilized as a valuable resource, especially by reclaiming nutrients and producing renewable products. Phyco-based or algal-based green technologies have garnered considerable importance in wastewater treatment (WWT) due to its efficiency to bioremediate contaminated water by assimilating the excess nutrients. Algae belong to group of photosynthetic organisms that can rapidly increase in size, reproduce quickly when conditions are ideal, and effectively absorb nutrients, making them especially suitable for applications in WWT and nutrient recovery. Apart from reducing pollutant loads, they also generate algal biomass that can be utilized in downstream applications, including biofuel, biofertilizers, animal feed production, and offering additional advantages in CO2 sequestration, low energy requirements, and minimal sludge production, making them a promising solution for sustainable wastewater management. Different cultivation techniques, such as traditional raceway ponds, advanced photobioreactors, membrane-based hybrid systems, and WWT wetlands, have been assessed for their effectiveness in various WWT situations, with each system offering significant advantages and drawbacks based on factors such as wastewater composition, climatic conditions, land availability, and operational costs. In the present chapter, we have comprehensively discussed the role of phyco-based systems in WWT, focusing on fundamental aspects such as nutrient uptake mechanisms, cultivation methods, and their implications for environmental sustainability. Despite many advantages of the system, future research should focus on key challenges such as optimization of cultivation conditions, developing novel algal strains having enhanced capability to nutrient uptake and resilience, and comprehensive cost-benefit analyses for large-scale implementation in WWT.