The application of this research focuses on the development of a wireless IoT approach for healthcare monitoring and analysis using machine learning techniques. The proposed system integrates sensors to measure vital signs such as pulse rate, oxygen saturation, and body temperature, and enables real-time visualization through a mobile application and LCD display. Data collected from the sensors are transmitted to the cloud via an Arduino microcontroller and Bluetooth, allowing healthcare professionals to remotely access and monitor the patient's health status. Two machine learning models, namely linear regression and artificial neural network (ANN), are employed to predict the health status based on the sensor readings. The ANN model achieves a remarkable accuracy of 100%, outperforming the linear regression model's 95% accuracy. This research highlights the potential of wireless IoT and machine learning in enhancing remote healthcare monitoring, enabling timely interventions and improved patient care. The findings contribute to the advancement of healthcare technologies and demonstrate the significance of remote health monitoring, particularly in challenging situations such as pandemics.