A novel approach for the optimization of vulcanization process was developed, which enables the determination of optimal operating parameters, time exposure and mold temperature, in order to obtain a high-quality products. The spheres of different diameters (2.5, 5, 10 and 20 cm) were selected for development of optimization model. Obtained vulcanization rheometer data of commercially available rubber blend was fitted using a novel approach, which devide the vulcanization curve into two fitting sets, curing and reversion.The accuracy of the developed fitting approach was examined with another type of rubber mixture based on natural rubber, with different content of biochar as a filler. High accuracy was obtained, where R2 values were not lower than 0.84 and MAPE values were not higher of 2.4%. The rubber compound with biochar as a filler was thoroughly tested by different methods in order to determine the possibility of carbon black partial replacement with biofiller. Commercially available carbon black can be replaced by 10 phr of biochar obtained from hardwood waste biomass, without significantly changing the mechanical properties of the final rubber product. It was introduced a new temperature-dependent parameter, named the reversion degree, enabling the determination of the lowest vulcanization operating temperature(Tmin = 132.36 °C), ensuring that reversion and product overheating do not occur. The dependence of the optimal temperature and vulcanization time on the dimensions of rubber products was determined, and the proposed model achieves a high degree of accuracy, where R2 values were greater than 0.8328 and MAPE was less than 2.3099 %. The heat transfer equations for the appropriate geometry were solved simultaneously with the developed kinetic model for simulation the vulcanization process of three spheres of different diameters and two rubber wheels, where one is a commercial product of the rubber industry. The proposed simulation model includes cooling after removing the product from the mold, as the crosslinking reactions continue to take place due to the warm interior of the product. The criteria for extracting the product from mold was set to value 0,9 of the average vulcanization degree, ensuring obtaining the appropriate shapes of rubber products. The optimization of vulcanization was carried out with obtained the minimum difference between the maximum and minimum vulcanization degree, enabling efficient process and obtaining high-quality homogeneous products. The optimal process parameters for all rubber products have been determined. An artificial neural network model was developed to predict the vulcanization rheological data of a commercially available rubber compound. The influence of three activation functions and different number of hidden layers and neurons, on the neural network prediction accuracy was examined, where by the neural network with the Softplus activation function and 20 neurons in two hidden layers predicts the torque dependence on time with high accuracy, where the MAPE and MSE values were lower than 2% and 0.032 dN2m2, respectively, and R2 values were higher than 0.98. The developed artificial neural network model was validated with a rubber mixture with biochar as a filler.