Hypertension (HTN) remains a leading cause of global morbidity and mortality, with its prevalence rapidly increasing in middle-income countries like Mexico. This study leverages data from the National Health and Nutrition Survey (ENSANUT) 2022–2023 to identify significant risk factors for HTN using machine learning models and explainable artificial intelligence techniques. Methods: We analyzed 8,650 Mexican adults aged \(\ge \) 20 years using logistic regression, random forest, and extreme gradient boosting (XGBoost) classifiers, optimized through Optuna framework. Hypertension was defined using ACC/AHA 2017 criteria ( \(\ge \) 130/80 mmHg or previous diagnosis). SHAP, LIME, and ELI5 were employed for model interpretation. Performance was evaluated using stratified 5-fold cross-validation and external validation on ENSANUT 2023 data. Results: XGBoost demonstrated superior predictive performance (ROC-AUC: 0.758, F1-score: 0.703) compared to logistic regression and random forest. Key risk factors identified were age, body mass index (BMI), gender, family history of hypertension (maternal and paternal), and physical activity—particularly sedentary behavior. SHAP-based subgroup analysis revealed that age and BMI consistently emerged as the most influential factors across both genders, with family history of hypertension (maternal and paternal) also showing significant importance. Gender-specific differences were subtle, with similar risk factor patterns observed in men and women. Conclusions: Machine learning models, particularly XGBoost with explainable AI techniques, effectively predict hypertension risk using nationally representative Mexican data. These findings support implementation of ML-driven risk stratification in primary care and inform targeted public health interventions. The integration of XAI methods provides transparent, interpretable predictions suitable for clinical decision-making and policy development in Mexico’s healthcare system.