<p>A decrease in estrogen levels in postmenopausal women triggers a cascade of physiological and pathophysiological changes (lipid imbalances, endothelial dysfunction, activation of the renin-angiotensin-aldosterone system), which increases by fivefold the risk of cardiovascular diseases, making them a leading cause of mortality (up to 35% of cases). Hormone replacement therapy is a proven method for relieving the undesirable symptoms of hypoestrogenism. The cardioprotective properties of endogenous estradiol have been confirmed, but the efficacy and safety of hormone replacement therapy in the setting of polypharmacy remain controversial. Some studies link hormone replacement therapy to an increased risk of heart attack, stroke, and thromboembolism, requiring strict monitoring of and a balance between the efficacy and safety of the therapy. A promising solution to this problem is the integration of artificial intelligence and machine learning algorithms based on predictive analysis of pharmacodynamic and pharmacokinetic interactions into science and medicine. Modern computer technologies can optimize treatment approaches by assisting physicians with clinical decision-making, predicting drug?drug interactions, identifying iatrogenic risks, and increasing the effectiveness of personalized treatment regimens, which is especially relevant for treating patients with polypharmacy.</p>