Our ability to manufacture metal oxide nanoparticles (NPs) with well-defined composition, morphology and properties is a key to accessing new materials that can have a revolutionary technological impact, for example for photocatalysis or storage of energy. Among the different nanopowders production technologies, Flame Spray Pyrolysis (FSP) constitutes a promising option for the industrial synthesis of NPs. This synthesis route is based on the rapid evaporation of a solution - solvent plus precursors - atomized in the form of droplets in a pilot flame to obtain nanoparticles. Unfortunately, mastery of the FSP process is currently limited due to too much variability in operating conditions to explore for the multitude of target nanoparticles. In this context, the objective of this thesis is to develop the experimental and numerical framework required by the future deployment of artificial intelligence for the control of FSP systems. To do this, the different phenomena taking place in the synthesis flames during the formation of the nanoparticles will be simulated, in particular by means of fluid dynamics calculations. Ultimately, the creation of a digital twin of the process is expected, which will provide a predictive approach for the choice of the synthesis parameters to be used to arrive at the desired material. This will drastically reduce the number of experiments to be carried out and in consequence the time to develop new grades of materials