A critical issue in education, particularly in developing nations, is the early and accurate prediction of youngsters leaving out. Truancy can be influenced by a variety of circumstances. Given that the task would need to be completed as soon to the conclusion of the school year as feasible for the best results, a standard categorization technique to solving the problem might not be stringent enough. I trained a number of machine learning algorithms, including a neural network, to develop the most accurate student dropout prediction model as quickly as feasible. The information used was received from 460 Indian high school students.