Patient health monitoring is the use of technology to continually measure and analyze a patient's vital signs and health data, allowing for early diagnosis of problems, better chronic disease management, and prompt treatments. This can be done remotely via smartwatches or fitness trackers, or in person using medical equipment. In this paper a novel has been proposed for patient health monitoring. Initially, a smart patient health monitoring system that collects vital health parameters such as blood sugar, heart rate monitors, temperature sensors, and smartphone applications. The collected data undergoes pre-processing, including normalization, tokenization, and data cleaning, to enhance accuracy and reliability. A DL-based classification model analyzes the processed data to categorize patient health status as either normal or abnormal. In the case of abnormal conditions, the system triggers an alert notification on the patient’s smartphone for immediate action. Additionally, patient data security is ensured through an authentication system that verifies user access via secure login credentials. This approach facilitates continuous health monitoring, early detection of medical anomalies, and timely intervention, thereby improving patient care and reducing healthcare risks.