Freshwater algal blooms have become a growing concern all around the world, which are caused by a high level of phytoplankton, particularly cyanobacteria that can produce cyanotoxins. The traditional manual counting of phytoplankton is mainly involving microscopic identification and counting of cells, which are limited by inaccuracy and time-consuming. As the development of molecular techniques and increasing number of microbial sequences are available in the GenBank database, the use of molecular methods can be used for more rapid, reliable, and accurate detection and quantification. However, the conventional molecular techniques, such as fluorescence in situ hybridization (FISH) and realtime qPCR, have difficulty in obtaining the complete profile of phytoplankton. In this study, metagenomic high-throughput analysis using an Ion Torrent Personal Genome Machine (PGM) was first adopted to investigate the phytoplankton community in Macau Storage Reservoir (MSR) that is recently experiencing cyanobacteria blooms. The present study showed that the total phytoplankton could be determined well through PGM. Totally 99,489 reads were recorded for phytoplankton, with 60.011 % Cyanobacteria, 39.442 % Chlorophyta, 0.001 % Euglenida, and 0.546 % Bacillariophyta. The innovative approach provides another reliable monitoring option, in addition to the traditional microscopic counting and conventional molecular techniques for ecosystem monitoring program.