The exponential growth of biomedical data, combined with advancements in machine learning (ML), has created opportunities for more precise diagnostics, enhanced treatment planning, and improved patient management. However, the successful implementation of ML in clinical settings depends on healthcare professionals’ understanding and competency in these technologies. This study examines the effectiveness of integrating ML methodologies into the curricula of Astana Medical University and S. D. Asfendiyarov Kazakh National Medical University. Focusing on childhood allergic conditions such as asthma, rhinitis, and skin diseases, a supervised ML approach (linear regression) was employed to analyze both clinical and educational data. Results demonstrated that the experimental group of students, who received ML-integrated training, exhibited significant improvements in analytical proficiency and data processing accuracy compared to a control group. The ML model achieved a coefficient of determination (R²) of 0.85, with low prediction errors (MAE = 0.45, MSE = 0.30, RMSE = 0.55). Statistical tests supported the hypothesis that structured ML education enhances medical students’ competencies, suggesting that future healthcare professionals trained in ML can better leverage data-driven decision-making for improved patient care. This study contributes to the growing body of literature advocating for ML integration in medical education and underscores the need for further research into advanced ML algorithms and long-term clinical outcomes.