In this work, we present a hybrid approach for the classification and evaluation of semiconductor defects using the adaptive neuro-fuzzy inference system and the technique for order preference by similarity to ideal solution multi-criteria decision-making method. The adaptive neuro-fuzzy inference systems model was trained using empirical data extracted from visual inspection systems, with input parameters including elongation, orientation, and spacing between defects. Two training algorithms–hybrid and backprop – were applied to optimize system performance, achieving classification accuracies of up to 85.1% with Root Mean Square Error values as low as 0.40. To determine the most suitable defect type, the technique for order preference by similarity to ideal solution method was employed using five criteria: geometrical characteristics, classification accuracy, and model error. Results indicate that grain boundaries exhibit the highest technique for order preference by similarity to ideal solution score, reflecting their stability and detectability under automated classification conditions. Dislocations and vacancies were ranked second and third, respectively. This dual-method approach provides a reliable framework for early defect identification and prioritization in two-dimensional semiconductors such as MoS2. The integration of machine learning with decision-making models enhances predictive capacity and supports defect-engineering strategies in advanced semiconductor design.