Algae biodiversity is incredibly high, with more than 50,000 described species with only an estimated 50% discovered, which represents a wealth of uncharacterised metabolic pathways and biocompounds. This wealth of bioproducts—and the potential thereof—is of growing appeal to the pharmaceutical, energy, and food sectors. Also, algae have been recognised as novel, sustainable and ethical sources of bioproducts. However, the systematic search for bioproducts in different species is challenging and time-consuming. Therefore, we propose a novel method for algal bioprospecting using modern omics data (genomics, proteomics, and metabolomics) allied with machine learning (ML) algorithms to predict potential new bioproducts and identify algae with untapped potential. Taking advantage of the Australian National Algae Culture Collection (ANACC) and the Great Southern Reef (GSR), a seaweed biodiversity hotspot, this study uses a wide taxonomical algal diversity to build a bioprospecting platform with a comprehensive multi-omics database to identify algae sources of bioproducts rapidly. This method requires low biomass for initial screening, facilitating the selection of target species and/or bioproducts for future laboratory experiments and decreasing time and costs. This unique combination of multi-omics data with ML represents a resource to accelerate future product development and biodiscovery sciences.