IN THIS RESEARCH, QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP OF AZOLES AS COPPER CORROSION INHIBITORS WAS STUDIED BY support vector MACHINE. FOR THIS PURPOSE, CORROSION INHIBITOR EFFICIENCY OF AZOLE COMPOUNDS (IN VARIOUS STRUCTURES) WAS COLLECTED FROM DIFFERENT REFERENCES. AFTER THAT STRUCTURE OF THESE COMPOUNDS WERE DRAWN AND OPTIMIZED BY HYPERC HEM SOFTWARE. MOLECULAR DESCRIPTORS OF AZOLES WERE EXTRACTED BY DRAGON SOFTWARE AND SELECTED BY PRINCIPLE COMPONENT ANALYSIS (PCA) METHOD. THESES STRUCTURAL DESCRIPTORS ALONG WITH ENVIRONMENTAL DESCRIPTORS (PH, TIME OF EXPOSED, TEMPERATURE AND CONCENTRATION OF INHIBITOR) WERE USED AS INPUT VARIABLES. ALSO CORROSION INHIBITOR EFFICIENCY OF AZOLES WAS USED AS OUTPUT VARIABLE. EXPERIMENTAL DATA WERE DIVIDED RANDOMLY INTO TWO SETS: TRAINING SET FOR MODEL BUILDING AND SIMULATION SET FOR MODEL VALIDATION. LINEAR MODELS WERE INVESTIGATED BY MULTIPLE LINEAR REGRESSIONS (MLR) AND MULTIPLE QUADRATIC REGRESSIONS (MQR). RESULTS SHOWED POOR CORRELATION BETWEEN EXPERIMENTAL DATA AND MODEL DATA IN LINEAR MODELS. HENCE NONLINEAR METHOD SUCH AS support vector MACHINE WAS USED FOR STUDYING NONLINEARITY OF DATA. THE MODEL WAS BUILT BY TRAINING SET AND VALIDATED BY SIMULATION SET. RESULTS SHOWED GOOD AGREEMENT BETWEEN EXPERIMENTAL AND THEORETICAL DATA THAT ACHIEVED BY SVM. HENCE SVM CAN BE USED AS A GOOD TOOL FOR PREDICTING AZOLE’S CORROSION INHIBITOR EFFICIENCY FOR COPPER IN THE PRESENCE OF ENVIRONMENTAL CONDITIONS.