Thyme is one of important medicinal plants that have been used since the past. This plant has many properties in the treatment of diseases, especially infectious diseases, thyme and its components are used in various industries such as pharmaceutical, food, cosmetics and health. In order to maintain the quality and quantity of essential oil extraction of plant drying process has a great role in the processing of medicinal plants. An important aspect of the drying technology with the aim of selecting the most appropriate drying method is mathematical modeling of the process. Therefore in this study, thin layer drying behavior of thyme (Thymus vulgaris L.)was experimentally investigated in a convective type dryer and the mathematical modeling performed by using adaptive neuro-fuzzy inference system (ANFIS). The drying experiments were conducted at inlet drying air temperatures of the 40, 50 and 60⁰C, at three drying air velocities of 1, 1.5 and 2 m/s. For kinetic model simulation of thin-layer drying of thyme, four ANFIS models were used, and to generate the fuzzy inference system model, the two partitioning techniques, grid partitioning and subtractive clustering, were used. Results indicated that ANFIS model could satisfactorily describe the drying curve of thyme. Also, comparison of the results of the two partitioning techniques showed that subtractive clustering technique was found to be the most suitable for fuzzy inference system generation for predicting moisture ratio of the thin layer drying of thyme.