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Electrochemical machining (ECM) is a potential nontraditional machining process that includes too many factors contributing in process performance. Therefore, obtaining optimal factor combination for higher efficiency is really complex. In the present work, in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage, and feed rate on material removal rate (MRR), surface roughness (SR), and radial overcut (ROC), manually constructed neuro-fuzzy inference systems have been used for creation predictive models based on experimental observations. Then, effects of process factors on MRR, SR, and ROC have been analyzed through plots which were drawn using developed fuzzy models. Then, principal component analysis (PCA) was applied on experimental data to allocate appropriate weight factors to the responses. Hereafter, the developed objective function which was obtained through the combination of neuro-fuzzy and PCA is associated with teaching cuckoo optimization algorithm to find optimal parameter combination causing maximum MRR as well as minimum SR and ROC, simultaneously.