Introduction. Optimizing the costs of pharmaceutical care for children is a particularly important task for the health care system. Children are one of the most vulnerable population groups and their health requires special attention and care. High costs of drug therapy of children's diseases become a significant financial burden, primarily for their families, as well as for the system of compulsory and voluntary health insurance. Modern approaches to the economic analysis of cost determinants in health care include methods of descriptive statistics, regression analysis and clustering. Objective. To examine the potential of using regression analysis to study the factors affecting the cost of pharmaceutical care for children. Materials and Methods. The study base included data on prescriptions of pharmaceuticals by physicians from the medical information system of the Moscow clinic network for 2023. Data analysis was performed using the «Python» programming language and «Jupyter Notebook», including the libraries «statsmodels», «scipy», and «pandas». Sample size calculations, normality test of the distribution, and detailed regression analysis were performed. Results. The database of drug prescriptions and their cost in Moscow pharmacies was studied. Factors affecting the cost of pharmaceutical care were identified: number of diagnoses, season, number of visits, average cost per patient, type of prescription, number of drugs, as well as sex, age and diagnosis of the patient. A robust regression analysis was performed, showing significant relationships between the cost of pharmaceutical care and a number of factors. Discussion. Factors such as diagnosis, number of visits and average cost per patient were found to have statistically insignificant or little effect on cost. The remaining factors showed a significant relationship with the cost of pharmaceutical care. Among other things, the fact of seasonal variation and the effect of time of year on the average cost of pharmaceutical care was found to be significant. Conclusions. Robust regression analysis showed that the selected model explains about 70,2% of cases. The results of the study can be used to optimize the costs of pharmaceutical care, develop effective drug insurance programs and improve the availability of medicines for children.