The aim of ABCRpred server is to predict resistance/susceptibility of any new beta-lactamase protein sequence towards ceftazidime antibiotic using machine learning approach. The machine-learning technique used 87 antibiotic-sensitive and 112 antibiotic-resistant beta-lactamases protein sequence data for training or testing. We tried different machine learning methods and achieved an AUROC of 0.8 and 0.79 of training and testing data using Random Forest method.