Cyanobacteria hold great potential to revolutionize conventional industries and farming practices with their light-driven chemical production. To fully exploit their photosynthetic capacity and enhance product yield, it is crucial to investigate their intricate interplay with the environment, in a particular light. Mathematical models provide valuable insights for optimising strategies in this pursuit. In this study, we present an ordinary differential equation-based model for cyanobacterium Synechocystis sp. PCC 6803 to assess its performance under various light sources, including monochromatic light. Our model accurately predicts the partitioning of electrons through four main pathways, O2 evolution, and the rate of carbon fixation. Additionally, it successfully captures chlorophyll fluorescence signals, enabling valuable information extraction. We explore state transition mechanisms, favouring PSII quenching over PBS detachment based on theoretical evidence. Moreover, we evaluate metabolic control for biotechnological production under diverse light colours and irradiances. By offering a comprehensive computational model of cyanobacterial photosynthesis, our work enhances understanding of light-dependent cyanobacterial behaviour and supports optimising their metabolism for industrial applications.