This study presents a real-time synthesis parameter control system designed to produce materials with specific physicochemical properties. By leveraging multiple Mamdani neuro-fuzzy models, we developed a quasi-dynamic fuzzy expert system capable of predicting key product characteristics, such as yield and crystal size, based on synthesis parameters like reaction time. The system processes input variables – porosity, crystal size, temperature, and precursor concentrations – to generate predictive outputs encompassing synthesis duration, final product concentration, and necessary thermostat power. The system was developed using experimental data from the solvothermal synthesis of UiO-66, a promising member of the metal-organic framework class. The neuro-fuzzy inference model outputs show close alignment with experimental data, underscoring its potential for optimizing MOF synthesis.