The aim of this work was to apply the INForm V5.1 GEP program in the development and optimization of different solid lipid nanoparticles (SLN) formulations using physical parameters to achieve the best combination of materials. SLN have been produced by high-shear homogenization, and the formulations have been characterized for their mean particle size, polydispersity index and zeta potential, and the most promising formulations in terms of physicochemical stability have been identified applying the INForm V5.1 GEP program. The data obtained from the laboratory results, following analysis of the variables allowed us to reach most appropriate formulation among 4 different types of SLN. It has been shown that when artificial intelligence is used in the product development phase, the desired result will be obtained with less experiments in shorter time.