AI-Driven Hypertension Detection and Risk Prediction: A Comprehensive Review
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Abstract
Hypertension is regarded as one of the most acute global social problems and etiological factors of cardiovascular diseases, stroke, and kidney failure. The early identification and appropriate risk forecasting are the keys to the reduction of morbidity and mortality caused by hypertension. Nevertheless, there have been recent advancements in artificial intelligence (AI) and, particularly, machine learning (ML) and deep learning (DL), which have significantly improved the capacity to detect hypertension and its risk when using various sources of data, such as clinical records, physiological data, and wearable sensor data. The literature review is based on a systematic method of literature selection and the analysis of the latest studies (2020-2025) of large scientific databases. The current review paper provides a general overview of the AI-based hypertension monitoring and prediction methods. It provides a literature review of the field of ML and DL models, datasets, feature engineering techniques, and signal-based monitoring systems. The paper also lists the strengths and weaknesses of the current methods, the problem of the data quality, as well as the interpretability of the model and clinical validation, and multiple future research directions have been specified. Using AI-based solutions is promising immense potential for delivering timely diagnosis, sustained care, and patient-focused care as far as hypertension is concerned.
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Copyright (c) 2026 Munawar S, et al.
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