In recent decades, machine learning has its increased problem solving methodologies and applications in various fields of business, marketing, education and medical diagnostics. Among all the ML techniques, some have been employed in the fields of medical sciences to predict various health conditions which require complete analysis of the patient’s health by considering some parameters which impact the health condition of an individual. Low birth weight of infants has been a constant recurring problem which has its visible impacts only after the birth of the baby. It acts as an indicator of sickness in newborn baby’s weight. This can be avoided using suitable ML techniques which predict required values from the information passed to the trained model. The prediction algorithm is implemented using random forest regression algorithms. We are aiming to develop a website which considers the health indicators as inputs from the user and provides them with estimated value by the trained ML model. The measures of metrics used here are the number of gestational days, age of mother, height, weight, plurality, and smoking status of the mother. All of these parameters can be used to estimate the accurate value of the weight of the baby within the web application.