<p>Renovascular hypertension is a kind of secondary hypertension that happens as a outcome of narrowing of the arteries feeding the kidneys and is often diagnosed late, leading to serious complications such as chronic kidney disease and cardiovascular issues. Early prediction of this condition is crucial for timely intervention and treatment. This study proposes a novel approach using medical data from ECG monitors and Recurrent Neural Networks (RNNs) to predict renovascular hypertension at an early stage. Continuous ECG data, alongside relevant physiological parameters, is collected through IoT-enabled wearable devices. The temporal patterns in this data are analyzed using RNNs, which are well-suited for handling sequential medical data. The model is trained to detect subtle irregularities in heart rhythms and other early biomarkers associated with renovascular hypertension, providing a predictive tool that can alert clinicians before symptoms become critical. Integrating ECG data with RNNs enhances diagnostic accuracy, surpassing traditional approaches. This method promotes early diagnosis, reduces reliance on invasive testing, and supports efficient hypertension management.</p>