The effect of poor governance in developing countries can lead to problems as to who is best to vote in elections. This is further affirmed by various political, social, economic and religious segregation, cultural bias and corruption. In quest for good governance and elections within the government without bias, there is a need to consider candidate's credibility. Therefore, this paper proposes a model for predicting credible candidates contesting in an electoral process. Moreover, the rationale of the paper is to assess candidate's credibility and the potential to win elections. Data are extracted from the opinion of elite Nigerians on Twitter and it was used to train the proposed model and to determine a reliable output from the opinions of personal credibility criteria of political leaders in Nigeria. In the proposed system, six (6) political candidates are sampled and the most credible candidate is selected. The data collected are subjected to Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for training and the Gaussian Membership Function (GMF) technique is adopted to calculate the membership grade for each input parameter implemented on MATLAB R2018a. The results obtained are the Average Testing Error (AVE) of 0.17 and the prediction accuracy of 92.85%, respectively. Comparative analysis of the current results shows the prediction of the electoral systems with significant contributions.