Monitoring blood pressure in real time and early on helps one prevent cardiac issues like heart attacks and strokes. Conventional methods of blood pressure measurement can fail to detect real-time abrupt changes or trends. This is hence why it’s crucial to design more sophisticated systems for constant tracking. This study aims to provide blood pressure monitoring more precisely and promptly using a blend of Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Recurrent Neural Networks (RNN). Early high blood pressure may be found using the SVM model, which classifies blood pressure values into normal and aberrant categories. By searching fresh data against existing data, k-NN finds outliers and thereby enhances anomaly detection. Particularly Long Short-Term Memory (LSTM) networks, RNN estimates future blood pressure patterns and detects minute changes throughout time. These algorithms are aggregated into S-KRNet, a single system that categorizes items, detects issues, and projects future health issues. The suggested hybrid model does better than each separate model in terms of F1-score, sensitivity, specificity, and accuracy, with a rate of 92.7% compared to 85.4% for SVM, 82.1% for k-NN, and 88.9% for RNN. The S-KRNet model takes a little longer to compute, but it makes sure that recognition is accurate in real time, which means it has a lot of real-world promise in healthcare settings. This model makes personalized, proactive patient care easier by letting quick alerts and actions happen. This makes it a useful tool for keeping an eye on blood pressure and managing heart diseases.