Cyanobacterial harmful algal blooms (CyanoHABs) are increasingly adversely affecting lakes and reservoirs throughout the world. Understanding the key factors influencing CyanoHAB dynamics can lead to improved predictions of CyanoHABs and the development of effective mitigation strategies. The formation and spatiotemporal distributions of the blooms are governed by complex physical mixing and transport processes that interact with physiological processes affecting the growth and loss of bloom-forming species. However, conventional models (i.e., population-level models) are unable to collectively consider the drivers and processes controlling CyanoHAB dynamics. In addition, cyanobacteria are well documented to use a wide range of adaptive mechanisms, yet conventional models do not take these mechanisms into account. This limits the conclusions that can be drawn from the simulation output, which calls for improvements to existing CyanoHAB models. This PhD research proposes that individual-based models (IBMs) are well suited to develop the next-generation of CyanoHAB models, and gaps and challenges in the use of IBMs for CyanoHAB modelling are identified. IBMs with the ability to track each individual cell and to retain and transfer physiological properties (e.g., light exposure and nutrient status) at a cellular level through successive time steps, can revolutionise the rather primitive CyanoHAB modelling efforts.