<p>The erratic nature of increasing climatic variability and changing temperature–rainfall patterns has resulted in changes in the distribution of <i>Hymenia recurvalis</i> (Fabricius) (Lepidoptera: Crambidae), a significant pest that affects leafy vegetable cultivation in Nigeria across the agroecological zones. Addressing the continual limitations encountered in local outbreak detection models, this investigation proposes a climate-orientated predictive system that utilises pest data sourced from ten vegetable production sites situated in different agroecological zones—Sahel, Sudan Savanna, Northern Guinea Savanna, Southern Guinea Savanna, and Forest—over a time frame of five years (2019–2023) to bolster integrated pest management. During the research, the areas of Katsina, Kano, Zaria, Abuja, and Ibadan were geographically located for exact analysis. NiMet provided meteorological data—temperature, rainfall, and humidity—analysing 600 georeferenced records using random forests along with logistic regression algorithms. Random Forest was eventually found to be much more efficient at predictions than logistic regression, with a strong 91% accuracy (AUC = 0.94) and high levels of other indicators as well. The regression analysis showed that both rainfall and the temperature minimum are the main factors responsible for the outbreak (<i>p</i> &lt; 0.01). The Random Forest model showed good predictive performance (89.7% ± 2.8%) and good average AUC (0.92 ± 0.03), which shows a strong association with past prevalent conditions and good generalisation ability by performing cross-validation to high standards. Technology is used by smallholders to address problems in the short term, safeguard crops against pests and increase food production under conditions of climate change.</p>