This study examines the Environmental Kuznets Curve (EKC) hypothesis in Central Asian countries as well as validates or invalidates the consistency of the different estimation results, using data, over the period 1992 to 2020. The study achieved this objective by employing various econometrics techniques such as Pooled Ordinary Least Squares (POLS) and Fixed-Effects Regression with Driscoll-Kraay standard errors, Panel Fully Modified Least Squares (FMOLS) Cointegrating Regression, Panel Vector Autoregression (PVAR) with Generalized Method of Moments (GMM) estimation, and Dynamic Panel-data estimation, One-Step system GMM. An empirical analysis uses the panel Unit Root tests to test the basics of the data unit based on this information. Following the best econometric practices, the descriptive statistics, Correlation matrix were computed to understand the characteristics of the variables and countries under analysis and to ensure that the necessary conditions for the estimation were fulfilled. The PVAR specification was based on the results of the Lag-order selection criteria, and the stability of the PVAR model was checked through the observation of the Instrumental variables GMM regression. The results of this study indicate that the GMM score supports the EKC hypothesis. This confirms the validity of the assumptions in the two inverse U-shaped EKC models between Gross Domestic Product (GDP) per capita in Central Asian countries and cereal production and agricultural land use. The results of this study show that the GMM assessment. That suggests that governments in Central Asia need to take the necessary initiatives to conserve agricultural land and encourage farmers to increase their arable land to meet the food needs of a growing population.