Remote Patient Monitoring (RPM) has become a cornerstone of digital health for chronic disease management, yet the clinical value of artificial intelligence (AI)–driven analytics depends on robust evidence using PhysioNet-informed simulated RPM dataset. This paper explores how machine learning models can be used to predict short-term clinical deterioration using a PhysioNet-informed simulated RPM dataset. Vital sign measurements are collected continuously. These data are used to check whether adverse clinical events occur within the following seven days.Several classification methods are applied in this work. The models include Logistic Regression, Decision Trees, Random Forest, Extra Trees, XGBoost, CatBoost, and Artificial Neural Networks. Each model is trained using the same dataset. The evaluation/testing of the models are carried out by performing time-ordered data splits. Performance is examined using discrimination, sensitivity, and calibration measures. In the results, boosting-based models such as XGBoost and CatBoost show higher performance than the other methods, including neural networks, when identifying early deterioration.These results suggest that remote patient monitoring systems based on such models may be useful in supporting clinical decision-making for chronic disease management.