Mathematical models can estimate the success rate of assisted reproductive technology (ART) and determine which treatment suits the best. In this study, the authors introduce two new dynamic models that can predict the success rate of ART models for each individual based on the etiology of their infertility, clinical findings, and lab tests. The study shows that the equilibrium point free of disease is asymptotically stable and forward bifurcation occurs. Our dynamical models evaluate ART success rates in our subgroups considering couples under Intrauterine insemination (IUI), In Vitro Fertilization–Embryo Transfer (IVF), Intra–Cytoplasmic Sperm Injection (ICSI) treatment methods. Sensitivity analysis of parameters suggests that an increase in infertile couples who use IUI and ICSI treatment methods can result in better disease control and a more favorable outcome. It should be noted that the proposed models provide a systematic framework for simulating the dynamics of infertility and assessing the effectiveness of various ART. From a clinical perspective, these models have the potential to enhance decision-making by predicting the probability of ART success under diverse treatment scenarios. Such predictions may facilitate the personalization of treatment strategies based on individual patient profiles and contribute to more efficient resource allocation and planning within fertility centers.

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