The Infection Prediction Framework is built on predictive displaying. The framework examines the client's side effects as well as their present and clinical history. System also determines the severity of infection and recommends treatment based on severity of the condition. It recommends a healthy diet and appropriate physical activity for the client. Expecting infection at a later stage becomes a considerable task. The Convolutional neural Network (CNN) model is used to anticipate such anomalies, as it can precisely identify information related to infection expectation from unstructured clinical health records. However, assuming that CNN uses a completely coupled network structure, it consumes a lot of memory. In addition, an increase in the number of layers might lead to an increase in the model's intricacy examination. The prediction of infection at an early stage becomes a critical task. This prediction can help people understand their potential stage of disease and take action accordingly as soon as possible. Prediction cannot be 100% correct as it is a probability statistic and cannot be always right. However, it can possibly be helpful in very serious situations and can lives. Keywords: Infection Prediction, Health Card, CNN (Convolutional Neural Networks), Smart disease prediction