This paper outlines the development of a prototype health monitoring system. This system integrates five specifically chosen sensors based on their plethora of connections and low cost, which address cost-efficiency concerns. The five sensors are heart rate, pulse oximetry, temperature, galvanic skin response (GSR), and blood glucose for diabetes monitoring. The Raspberry Pi 5 was selected as the controller for this system. This addresses accessibility concerns since it’s an open-source platform. In today's world, artificial intelligence represents a significant breakthrough. As a result, machine learning algorithms were utilised for this project. Several machine learning models were tested to choose the most suitable and appropriate for anomaly identification and data analysis. This paper also presents the integration of a mobile application for visualising the data and offering insights into the user’s health status. This system aims to monitor a person’s health data in real time, facilitating early detection of potential health issues.