<p>Over the last two decades, biocementation as a sustainable method for producing self-healing cementitious materials has received a lot of attention in construction, soil stabilization, and wind-induced erosion industries. Despite advancements in the experimental characterization of this phenomenon, computational modeling of this biochemical process has not significantly advanced due to its complexity and the interrelated parameters involved. This study introduces a computational model in COMSOL Multiphysics^® to simulate calcium carbonate (<i>CaCO</i>_<i>3</i>) precipitation by <i>Sporosarcina pasteurii</i>. A theoretical background on the interconnected parameters governing the precipitation process and on the concurrent chemical reactions involved is presented. The workflow and sequential implementation of input parameters are detailed. The model considers the impact of calcium and urea concentrations, alongside the initial pH level (ranging from 0 to 14) and the pH fluctuations induced by by-product generation during the biocementation&#xa0;process, at a constant temperature. The capability of the model to foresee <i>CaCO</i>_<i>3</i> concentration and ultimate pH level is evaluated by comparing the computational outcomes with empirical data obtained from established literature sources. The coefficient of determination, R², exceeding 0.99 indicates a strong correlation between experimental and numerical data, demonstrating the model’s high accuracy in predicting the quantities of calcium carbonate and other biocementation products and by-products. The influence of the initial pH of the environment on the pH variation of the system during the precipitation process was simulated and compared to actual data available in the literature, suggesting that the model can accurately predict the kinetic of biocementation. Through a parametric study, an environment with an initial pH between 4 and 10 was found to be the most favorable for biocementation by <i>S. pasteurii</i>.</p>