This study explores the influence of climate change on agricultural productivity in the East African Community (EAC), a region heavily dependent on agriculture for food security and economic stability. The study employs a mixed-methods strategy to analyse the impact of crucial climate factors such as carbon dioxide (CO2) levels, rainfall, higher temperature, and sun radiation on cereal yields across EAC member states over the last three decades. Applying dynamic panel data techniques, this study Implemented first-order differencing to remove unobserved effects and employ the Generalized Method of Moments (GMM) estimation for robust analysis. Based on 192 observations from six countries, the fixed-effects model reveals that CO2 concentration positively affects cereal yields, with a rise of nearly 2000.87 units per unit increase in CO2 (p = 0.002). Conversely, maximum temperature and solar radiation negatively affect production, declining it by 37.42 and 467.72 units, respectively (p = 0.062 and p = 0.007). This research discovers compelling evidence of homoskedasticity (Chi2(6) = 1755.38, p < 0.001) and no serial correlation (F (1, 5) = 6.570, p = 0.0504). To address potential endogeneity, the study implemented GMM estimation using lagged response Variables as instruments. The Arellano-Bond test shows no significant second-order autocorrelation (z = −0.87, p = 0.384), validating my approach. The two-step GMM results expose that all variables are essential, highlighting the complex interactions between climate variables and agricultural productivity.