Integrating monothematic data from reductionist methods used to understand the biological processes of algae remains a significant challenge. However, due to the abundance of algal genomes, transcriptomes, and proteomes, the improved systems biology approaches are shifting to interdisciplinary data integration to better understand the biological system behavior and high-throughput data content. Owing to the biotechnological significance of algae, integrated systems biology is progressing to transform and improve the data landscape in phycology. In this stance, systems biology comprises modeling techniques such as genome-scale metabolic models (GEMs) and machine learning (ML) methods used to analyze complementary omics data. In particular, genomics has been utilized to obtain diverse algal taxa in their natural habitats, allow gene manipulations, and identify gene-encoding algal metabolites such as primary and secondary metabolites. While transcriptomics has been used to explore the evolutionary algal transcriptome (complete set of RNA molecules) and understand the patterns of genomic expression into proteins in correspondence to environmental stressors, proteomics evaluates the protein (primary metabolites) structural differentiation and identification, functionality, quantification, and protein-protein interactions. Systems biology sheds light on understanding algal physiology and ecology. In light of this, this chapter underlines the systems biology approach with integrated high-throughput technology (genomic, transcriptomic, and proteomic) data of algae. It also describes the omics data integration methodologies, challenges, and merits in systems biology and algal perspectives. Overall integration of omics data is a rapidly developing field, and it is expected to enhance the omics algorithms in learning algal metabolism.