Purpose: This paper attempts to explore possible contributing factors of females' outperformance and males' underperformance in the higher education in Pakistan from teachers' perspective. The central question of the study is what are the key factors that affect female and male students' educational performance at the university level? Using Artificial Neural Network (ANN) as a framework, we attempted to predict differentials of the perceived "female outperformance" and "male underperformance" in higher education. We carried out the study by employing quantitative research methods. Design/methodology/approach: The data for the study come from 253 teachers from University of the Punjab--largest and oldest University in Pakistan. We used a structured questionnaire for data collection. The analysis was carried out with the help of ANN model. Statistical Package for Social Sciences (SPSS) was used to analyze the data. Findings: The testing results of ANN indicated 85.3% of teachers' perception was correctly predicted on various dimensions of performance differentials across female and male students in higher education. Research limitations/implications: The study banks on primary data collected from teachers of the University of University of the Punjab, Pakistan. Thus, the study's universe was limited to one university--University of Punjab. It is purely based on a quantitative approach employing ANN. Practical implications: The findings of this study have several significant implications, i.e. it makes a significant contribution to the existing body of scholarly texts on the issue of gender reverse change in academic performance in higher education. Originality/value: The findings of this research, derived from primary data in Pakistan context, qualify this research as an original one. We also claim that this study is one of the first studies on gender reverse change in academic performance among graduate students in a public sector university of Pakistan employing ANN.