This project uses machine learning to predict the classification of microstrip circular patch antenna design based on its dimensions and performance such as resonance and impedance matching. A circular patch antenna was designed using Computer Simulation Technology (CST) microwave studio software. Followingly, a parameter sweep was conducted and a dataset of antenna performance characteristics was collected to train the ML model. Support Vector Machine (SVM) algorithm is the most effective at classifying antenna acceptability with 96% accuracy. A graphical user interface (GUI) is developed to allow engineers to easily input antenna dimensions and get a prediction on the acceptability. Classifying the antenna using machine learning technique helps engineers to reduce the time consumption for antenna optimization and classification and therefore enhancing the efficiency and accuracy of antenna performance evaluation.