Groundwater management is key to attaining sustainable development goals, especially in arid and semi-arid countries. Hence, a precise estimate of the aquifer hydrodynamic parameters (hydraulic conductivity, transmissivity, specific yield, and storage coefficient) is required for proper groundwater resource management. The central goal of this research is to utilize machine learning models to estimate transmissivity by pumping test data in unconfined alluvial aquifers. Artificial neural network (ANN), gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), fuzzy logic (FL), least square support vector machine (LSSVM), and group method of data handling (GMDH) methods were utilized to estimate transmissivity. To achieve this goal, pumping tests and hydrogeological data from 96 pumping wells located in the central plateau of Iran were collected and normalized. Using the existing normalized data, several combinations were utilized as inputs to the models, and then randomly, 70% of the data were used for the training step and 30% for the testing step. Finally, ten combinations that provided a better answer were selected from whole combinations. The mean absolute error, root means square error and correlation coefficient were used to assess the models' precision. The comparison criteria revealed that while all developed methods could provide desirable transmissivity estimations, the GMDH model was the most accurate and precise. The result of the models suggested that the best combination in transmissivity estimating was combination 3 (well discharge, thickness, depth of pumping well, and minimum and maximum time-drawdown data). Therefore, using this method and without graphic methods, it is feasible to estimate the transmissivity with acceptable accuracy in unconfined aquifers with similar hydrogeological and geological characteristics.