The increase in atmospheric concentrations of carbon dioxide, methane, and other damaging gases can lead to catastrophic repercussions to life as we know it. Therefore, reducing GHG (Greenhouse gas) emissions and fomenting strategies for their mitigation are crucial steps that need to be taken in order to meet the Paris Agreement and ultimately ensure a sustainable future for our planet and those that inhabit it. This work aims at identifying the metabolic capabilities of photosynthetic microorganisms to reduce methane emissions. Genome-scale metabolic (GSM) models allow the in silico simulation and prediction of metabolic fluxes on a large scale, providing a powerful tool for optimizing and designing metabolic engineering methods. Herein, we describe the reconstruction of GSM models for the microalga Chlorella vulgaris sp. – iGA1312 –, and for the cyanobacterium Synechocystis sp. PCC6803 – iJG707. Both GSM models provide a powerful tool for metabolic improvement, setting the basis for predictions and simulations of methane (CH4) metabolism in response to different culture conditions and genetic modifications.