Acoustic emission (AE) has gained much attention in recent years for its effectiveness in nondestructive testing and continuous monitoring of civil infrastructure. As a phenomenon, when structures are loaded, AE sensors mounted on the surface of the structure receive the waves released from the damage and convert them into electrical signals. By analyzing and evaluating these recorded signals, critical information such as type, location, time of origin, size, and orientation of the damage can be identified. Since the whole process, which includes a massive amount of dataset, is time-consuming and needs to be automated, this study aimed to develop a machine learning–based damage estimation model that would estimate fracture characteristics of concrete and would be a pioneer for the application of real-time and automated monitoring of structures. In this scope, failure behavior of concrete samples of various strengths and sizes under loading was monitored with AE using K-nearest neighbor (KNN) algorithm. A relationship was established between the load levels and damage status of the specimens with AE features. Afterward, the model was trained and tested, and fracture characteristic results estimated by the KNN models were evaluated to reveal its feasibility.