This research shows a new smart health monitoring system using enhanced mathematical models and algorithms for enhanced prediction and real-time assessment of health conditions. The heavy dataset is handled by four algorithms namely Linear Regression, Support Vector Machine (SVM), Long Short Term Memory (LSTM) Networks, and kNearest Neighbour (k-NN) to determine a proper modelling of health metrics. The LSTM model provided the best results giving an accuracy of about 92% and mean absolute error of about 0. 35, outperforming other models. The SVM reached an accuracy of 88% : Precision = 0. 85 numbers of exposures to advert and recall of 0. 87, k-NN had a good accuracy of 85% and F1 score of . 83. Linear Regression, as one may expect performs worse for non-linear data, but it had a Mean Absolute Error (MAE) of 0. 45. The use of digital twin and wearable sensors also improves the work of the system and its capacity for detecting anomalies and dynamic health conditions. This research mainly focuses on the use and integration of the algorithms and technologies to design an efficient health monitoring system that can generate true and efficient health assessment.