Objective: The purpose of this research was  to predict the tonsillitis using machine learning algorithms. Increasing utilization of smartphones with sensor systems and machine learning capability promise better M-Healthcare services. Tonsillitis is an inflammation of your tonsils.  Tonsillitis analysis requires contemporary technology.

Method: Different machine learning algorithms and frameworks used for evaluating the accuracy and performance.Artificial Neural Networks combined with picture processing and RGB color coding used for identify tonsillitis early and monitor prognosis at home. This study describes an innovative machine learning and  smartphone-based optimization approach with a linked camera.

Results:Patients in remote locations, poor and impoverished countries may check, assess, and frequently do tonsillitis exams anywhere, anytime, and any place.  This research proposes an unique method and machine learning approach to evaluate tonsillitis photos and diagnose infections with 90\% accuracy for Random Forest and Decision Tree, .

Conclusion:In this research paper, we have introduced an advanced MHealth application on human health and monitoring systems. The use and technological advancement of smartphones has skyrocketed in the last decade. Now embedded sensors in smartphone devices help to assess physiological indicators and evaluate the health status. We have demonstrated that M-health can be effectively applied in the detection of tonsillitis by using smartphone devices and machine learning