Climatic factors play a primary role in crop‐yield fluctuations, with their effects exhibiting significant nonlinearity. This study examines the influence of air temperature throughout the growing season on wheat yield in the Steppe zone of Ukraine. Weather and climatic conditions in April, May, and June are critical for determining the subsequent wheat harvest. Based on a dataset of 150 climate records and using machine learning techniques, we constructed a statistically significant quadratic regression model to relate yield to temperature indicators. The resulting quadratic model served as the objective function, defined over a constrained domain of admissible temperature values. To locate the maximum of this yield function, optimization methods for multivariate nonlinear functions were employed. The coordinates of the maximum define the optimal temperature trajectory that ensures the highest wheat yield in the Steppe zone. To verify the results obtained, a comparison was made with optimization results produced by other machine learning methods. The proposed methodology enables early yield forecasting with a lead time of approximately three months and can be adapted to forecast the yields of other agricultural crops.