Determining a reliable model to predict the compressive strength of a geopolymer is important for finding better options for Portland cement, which has many disadvantages such as weaker physicochemical properties and a production with a large carbon footprint. For this purpose, an Exploratory Data Analysis (EDA) was performed with the information from experimental tests. This EDA allowed training an accurate machine learning model for compressive strength prediction. Various individual models and mixtures of models were tested, and the decision was made to utilize the best-performing individual model. The obtained results are similar to the literature in which the best models are based on decision trees and boosting algorithms. In addition, a simple and user-friendly interface was developed for making predictions using the selected model.