Today Internet of Things (IoT) finds numerous industrial applications. Possibility to apply large sensors networks and use such approaches as data clouds and edge computing significantly enhance efficiency of process control and prediction. Microalgae pigment production is based on the biomass cultivation and in many cases under stress conditions, with precise adherence to certain cultivation parameters. There are a lot of parameters should be controlled in the large area with photobioreactors (PBR). IoT application with combination of edge computing for local tasks and cloud computing for more general can improve efficiency of control and give prediction of pigment content and biomass productivity. This chapter provides review of recent IoT technologies that can be applied for microalgae pigment production with respect to its overall efficiency. The sensor level will be discussed in detail with analysis of edge computing application possibilities. The control tasks that require edge computing application are defined and discussed. The data fusion issues from sensors level to cloud level in the focus of review also. Application of IoT is increasing available data for process control and also increasing energy consumption for data storage and operation, this issue is discussed in the review additionally. Finally, analysis of recent researches in the field of machine learning applications for microalgae pigment production is provided with respect to possible IoT application.