It has taken ages for women to realize that no knight is coming to save their world. Although AI and its counterparts have drastically modernized our lives, gender equality in education still appears to be a distant dream across the globe. This paper aims at benchmarking machine learning techniques that exert the power of inclusive AI to analyze the potential causes for gender inequality in academia. Although ocean-wide disoriented datasets on maternal health, gender pay-gaps, digital literary, child labor and violence flood the storage on cloud, conclusive studies on the influence of this data in promoting/tapering gender equality in education is limited. The two major contributions of this paper are (a) Creating a new dataset to explore the influence of violence, child labor, geo-graphic distribution and culture on the current state of gender gaps in the work-force. (b) Employ data visualization and association rule mining to extract the potential factors that need to be addressed to expedite the achievement of safe and unbiased work environments across continents. The work records child labor, maternal leave benefits and reduced acceptance to violence as the significant components that can impair/improve equality in academia and higher education. Moreover, presence of legal rules to protect women, and reduced acceptance of violence by men are nurturing factors for promoting women at work. The results are substantiated by the Pearson and Spearman correlation that suggests the important factors that support or reduce gender bias in the workforce.