In 2021, cardiovascular diseases were responsible for 19.4 million deaths worldwide. Cardiac cryoablation is an effective therapeutic technique for treating arrhythmias, particularly in cases of atrial fibrillation, where extremely low temperatures are used to destroy, in a controlled manner, the tissue responsible for the abnormal conduction of electrical impulses. However, the success of the ablation depends on the precision and extent of the damage caused to the cardiac tissue, which can be influenced by various factors such as biological variability, individual anatomical conditions, and the accuracy of the procedure. In this context, this study uses mathematical modeling to simulate the cryoablation process and quantify the uncertainties associated with the damage caused to the cardiac tissue. Uncertainty analysis, using Monte Carlo methods, is applied to evaluate how different conditions influence the outcome of the ablation, providing a valuable tool for treatment personalization and advancing in silico medicine. The numerical results show that different freeze-thaw circles of cryoablation scenarios influence the temperature distribution, and the duration of the initial freezing phase also influences the extent of the region impacted by the freeze-thaw cycles. This model allows for analyzing and optimizing established cryoablation scenarios and developing new ablation strategies for future cardiac cryoablation studies. This technology may contribute to a higher clinical acceptance for treating cardiac arrhythmias.