This research paper discusses the use of machine learning techniques and their implementation to predict sepsis at an early stage. The study examines different machine learning algorithms that can be used to predict sepsis at an early stage along with the evaluation based on various parameters such as their performance and accuracy. Sepsis is activated by the immune system present in our body that works all the time to protect our body from various possible infections from entering. The main work of our immune system is to fight against bodily infections that may be due to bacteria or various other reasons and during this stage, enormous amount of synthetic substances is discharged into the blood. Sepsis occurs when body’s response to these chemicals is out of balance, which can damage multiple organ substances. For the patient the practicality of predicting sepsis disease occurrence in development is an important factor in the result. The primary goal of this study is aims to evaluate the performance of various machine learning algorithms for the early detection of sepsis and choose the best one based on the performance metrics to detect sepsis disease in minimal time. Our secondary goal is to build and design a user-friendly web application. The research results indicate that random forest outperforms other considered machine learning models with an accuracy of 97% which suggests its significant potential in early stage sepsis detection.