Knowing the weather conditions at airports is of vital importance for reasons that affect the safety, efficiency, and comfort of flights. Bad weather, such as strong winds or fog, can represent a significant danger to aircraft during takeoffs, landings, and flights. Therefore, learning to predict weather behavior, based on prior information, is important. That is why this research on the prediction of weather conditions at Jorge Chávez Airport in Lima is presented. To do this, a set of previous data was used to which the J48, Random Forest, SVM, Bayes Net, and Neural Network algorithms were applied to identify that the Random Forest algorithm obtained the best behavior with Accuracy = 76.8004% for the training and validation process; and Accuracy = 78.5181% for the test process. As a proof of concept, a Java application was implemented.