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. From an initial set of 132 climatic data records, 24 observations associated with high yield values were selected. Using machine‐learning techniques, a statistically significant quadratic regression model was constructed 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 yields the highest wheat output in the Steppe zone of Ukraine. The findings enable the development of early dynamic yield forecasts with a lead time of three to four months. The proposed methodology can also be applied to forecast the yields of other agricultural crops.