The integration of advanced machine learning (ML) techniques with density functional theory (DFT) has significantly enhanced the optimization and prediction of stable material structures. However, translating these computational predictions into successful laboratory syntheses—whether by autonomous labs or human scientists remains time- and cost-intensive due to the complex optimization of solid-state reaction parameters. In this work, we present the first application of a Reaction Graph Network (RGN) to model precursor interactions in inorganic reactions and predict synthesis conditions based on solid state reactions. Our approach enables the efficient prediction of synthesis conditions and demonstrates improvements over previous methods. This streamlines the path from computational predictions to material synthesis and offers potential to accelerate materials discovery.