Cement manufacturing is widely recognized for its harmful impacts on the natural environment. In recent years, efforts have been made to improve the sustainability of cement manufacturing through the use of renewable energy, the capture of CO2 emissions, and partial replacement of cement with supplementary cementitious materials. To further enhance sustainability, optimizing the cement manufacturing process is essential. This can be achieved through the prediction and optimization of clinker phases in relation to chemical compositions of raw materials and manufacturing conditions. Cement clinkers are produced by heating raw materials in kilns, where both raw material compositions and processing conditions dictate the final chemical makeup of the clinkers. This study uses thermodynamic simulations to analyze phase assemblages of alite- and belite-enriched clinkers based on chemical compositions of raw materials and to create a database. The thermodynamic simulations can accurately reproduce clinker phases in comparison with experimental results. Subsequently, the simulated database is employed to train a data-informed model, and the predictions are used to determine the optimal composition domains that produce high quality clinker (C3S>50 %) at different calcination temperatures. Additionally, optimal lime saturation factor and alumina modulus are investigated to achieve target clinker phases. Overall, this study demonstrates the potential of using a data-informed approach to achieve smart and sustainable cement manufacturing process.