Students and instructors’ performance analyses are one of the major issues in today’s education programs due to the quality evaluation and management approaches. Today, design program course targets are usually related with the program outputs intuitively and subjectively based on the past experiences of the instructors. Current proof methods are not very reliable and have difficulties adapting changes, in addition the new accreditation criteria on the agenda such as Bologna Process makes the requirement of more reliable proof methods inevitable. The aim of this article is to investigate the potential of the proposed adaptive neuro-fuzzy inference system (ANFIS) with a Sugeno type engine that can be flexibly trained to analyze and learn the relations between the selected program inputs and output, namely, the pre-design studio courses and the design studio relation for instructors’ evaluation performance analyses. The ANFIS method is applied in two institutions and the results of both applications are showing that the ANFIS model has performed well and is an optimal and reliable way of measuring and analyzing the correlations of the inputs and outputs of the program.