<p>This study examined the influence of land surface temperature and sociodemographic factors on measles prevalence in Kaduna North, Nigeria. The study used measles data collected from the Health Department, Kaduna North, and sociodemographic data (population density, number of healthcare facilities, vaccination coverage, and level of education) was obtained from the Kaduna State Bureau of Statistics. MODIS (MOD11A2) Land Surface Temperature (LST) data was extracted using the Google Earth Engine platform. The Mann–Kendall trend test was used to assess the linear trend of the LST and measles, while the relationship between the predictors and measles cases was assessed using the Pearson Correlation Coefficient and AutoML machine learning algorithms. The study revealed decreasing measles trends (Z = − 1.092) and LST (Z = − 0.438). For the relationship between the predictors and measles, population density and measles had a strong positive correlation (<i>r</i> = 0.822), but a moderate negative relationship with vaccination coverage (r = − 0.53). The H2O AutoML revealed that the Stacked Ensemble was the best-performing model. The cross-validation metrics were R² = 0.6585, MAE = 0.8464 and RMSE = 1.1987. This explained 66% of the data variation. The SHAP Additive Explanation population density is the most influential factor for the increase in measles cases, followed by LST. However, vaccination had a negative influence on measles cases. The study recommended enhanced sensitization of the population on the benefits of measles vaccination to curb the spread of the disease.</p>