The synthetic rubber industry is of great importance and is present in the daily life of world society. Butadiene rubber or polybutadiene is one of the most used polymers in this field, mainly in the production of tires. Therefore, controlling the operating conditions and the final properties of the polymer formed are important points to be studied, as they are a challenge for the industry. Thus, the present work focuses on simulating the polymerization in solution of polybutadiene using the Aspen Plus software, where 1,3-butadiene, titanium tetrachloride, triethylaluminum and hexane were used as monomer, catalyst, cocatalyst and solvent, respectively. From the work, molar mass distribution graphs were obtained that showed properties similar to commercial polybutadienes and some polybutadienes synthesized on a bench scale found in the literature. Furthermore, in a second part of the work, the instant distribution technique is studied and explained and how a database was generated for a machine learning model called XGBoost, where points from the MMD (molar mass distribution) graphs of the polymer served as input to the model in order to predict the kinetic constants of polymerization. Both studies and simulations show that three and four sites of active catalysts are able to synthesize polymers with properties similar to commercial and bench-scale polybutadienes.