Millet yield per unit area in northern China's drylands is constrained by climate, soil, and management factors, complicating forecasts under limited, nonlinear, heterogeneous data. In order to enhance the accuracy and stability of operational forecasting, this study utilised observational data from five locations in southwestern Heilongjiang Province spanning 2014 to 2023. Eight ground-based hydrothermal and meteorological factors were used as inputs to build an improved BP neural network optimised by IWOA, with enhancements to both algorithm and workflow. Adaptive inertia weight and EOBL were introduced to balance global exploration and local exploitation, enabling better hyperparameter solutions. Results show that IWOA-BP significantly outperforms baseline BP and WOA-BP on an annual scale. The RMSE was 2.74, the R2 was 0.94, the MAPE was 5.9, and the RPD was 4.16. The implementation of additional seasonal rolling forecasts for the 2024 validation period entailed the construction of cumulative information flows from January to August. Cross-regional validation in Fangzheng County produced error magnitudes consistent with the primary study area, thereby demonstrating the model's reliable generalization ability across both temporal and spatial dimensions.