Clay is an essential resource for improving the brick and ceramic industries. However, the demand for clay needs to grow. Estimating industrial mineral reserves requires an optimal and unbiased statistical tool. Geographical information systems and geostatistics are two promising tools that could be used to predict clay resources. The study objective was to apply these two tools to compare the performance of the methods for spatial clay resource prediction, considering clay thickness and spatial distribution. The demonstration site is in Tam Binh district, Vinh Long Province, Viet Nam. The results show that the Gaussian variation model at a 600 × 400 m distance is good for sampling and extrapolating clay thickness. This is based on 240 geolocated samples with different densities. Projected clay reserves match measurements, and the spatial statistical method is more accessible and valuable than the traditional method. The interpolated method’s total selected soil borehole number was optimized for clay resource estimation, and geostatistics can help manage and plan clay resources and save money. Future research should examine various spatial interpolation techniques and approaches in management, land use, and topography to improve the accuracy of calculations.