The process of selecting faculty teaching staff in higher education institutions traditionally relies on complex procedures, including resume reviews, interviews, and assessments of academic and teaching proficiency. This study aims to explore the potential of implementing an e-selection system as an innovative solution to improve the efficiency and accuracy of recruitment processes. By leveraging Artificial Intelligence (AI) and Artificial Neural Networks within the framework of Electronic Human Resource Management (e-HRM), the study seeks to automate and streamline multiple stages of the hiring process, such as initial screening and applicant evaluation. Data was collected from 512 applicants for academic positions, where 301 were accepted, and 211 were rejected by the human resource manager. The e-selection system was based on these data to train a neural network using predefined criteria for selecting faculty staff, including academic, personal, professional, and linguistic skills. The model achieved an accuracy of 97.7% in automatically evaluating applicants, reducing the need for human intervention and accelerating the decision-making process. This study highlights multiple benefits of using AI in e-selection, such as increasing efficiency in terms of time and cost, improving selection accuracy, reducing bias, and ensuring greater transparency and fairness in decisions. The research also discusses the challenges and ethical considerations related to implementing AI systems in this context, including privacy and transparency concerns. The findings of this study provide significant insights into how technology can be used to enhance traditional recruitment processes in higher education institutions.