Genome-scale metabolic models have been proven to be valuable for defining cancer or to indicate the severity of cancer. However, identifying effective metabolic drug target (DT) of the active small-molecule compound is difficult to unravel and needs to be investigated. In this study, we identify effective DT for breast cancer using proposed network analysis of enzyme-centric network in the metabolic model. Our network-based analysis revealed that high degree nodes (HDNs) of enzymes are key to progression/development of cancer. These HDNs show high interconnections inside the network. It has been found that these HDNs are crucial driver nodes for effectively targeting in breast cancer metabolic network. Furthermore, based on the correlation and principal component analysis, we have shown that certain proteins play a significant role in the network and can be used as an effective DT in cancer therapeutics. In addition, these proteins stimulate the active site of enzymes to activate the target metabolites. Overall, we have shown that a better understanding of the metabolic networks using statistical model could be valuable in DT identification for developing effective therapeutic approaches and personalized medicine.