This study by Dagnaw et al. applied machine learning to predict adverse drug reactions (ADRs) among HIV patients undergoing antiretroviral therapy (ART) at the University of Gondar Comprehensive and Specialized Hospital in Ethiopia. Using five years of electronic health record data (2018–2023) from 5,864 patients (64% female), the researchers trained seven supervised classification models in Python to identify patterns associated with ADR risk. Model performance was compared using the F1‐score, AUC, accuracy, sensitivity, specificity, and other precision metrics. The random forest algorithm significantly outperformed other models, achieving near‐perfect results (sensitivity = 1.00, precision = 0.987, F1 = 0.993, AUC = 0.9989), which indicates strong predictive reliability. Feature importance and association rule mining highlighted CD4 count as the strongest determinant of ADRs, followed by patient sex (male), younger age, longer ART duration, lack of co-trimoxazole or tuberculosis preventive therapy, lower education level, use of the TDF-3TC-EFV drug regimen, and low CD4 counts. Additionally, the integration of data mining methods showed clear patterns that could help providers categorize patients by risk level and identify combinations of clinical and behavioral risk factors.