Contribution: The proposed work carries out the training and testing of the available data through an artificial neural network and develops a model to allocate the subject for maximum outcome. The system also provides percentagewise correlation among all the possible subjects of best fit to allocate among the faculty members. Background: Data mining and machine learning tools have amazed all professionals with their fast, accurate, precise, and feasible results. While their results cannot be directly superimposed on all education systems, they certainly provide ideas for improving teaching pedagogy based on the requirements and capabilities of the system. Intended Outcomes: The subject allocation among the faculty members in engineering studies plays a crucial role in teaching and training the students in the best possible way from the point of view of outcome-based education. The objective of this article is to present an effective model for subject allocation to faculty members based on various factors. Application Design: Faculty members have their diversified strengths because of their involvement in different institute activities. An appropriate subject allocation mechanism for any faculty accumulating the knowledge of an individual’s responsibilities and area of interest can support more significantly in achieving the course outcomes. Findings: 1) Subject allocation based on individuals’ involvement in academics, administrative, and research domains; 2) Subject allocation based on qualifications and experiences for engendering the outcome; and 3) A user-friendly model development for applying at an individual, department, or even at the institute level.