This study applies logistic Small Area Estimation (SAE) to merge the 2022 National Survey of Employment, Unemployment, and Underemployment (ENEMDU) with the 2022 Population and Housing Census, generating an income-poverty map for Ecuador at the cantonal and parish scales. The model links harmonized census microdata with survey outcomes using a set of predictors that capture housing quality (materials, crowding, energy, and sanitation), the demographic profile of household heads (sex, age, schooling, ethnicity, and social security coverage), and geographic location (province, canton, and parish). Probabilities estimated for each of the 4.4 million census households yield a national poverty rate of 39.8%. The burden is highly uneven: rural areas register 60.7% versus 27.8% in cities, and female-headed households reach 46.8% against 35.5% for male-headed ones. Spatially, the Amazonian provinces exhibit the most significant deprivation, whereas Galápagos and Azuay display the lowest incidence. Model diagnostics support statistical robustness: deviance falls by about 29,000 units (from 90,788 to 61,498), McFadden’s R2 equals 0.32, and AIC/BIC values (61,718/62,771) indicate a sound balance between explanatory power and parsimony. The confusion matrix reports an overall accuracy of 88.3% and a specificity of 96.5%, confirming the reliability of household classification. By filling the long-standing gap left by nationally representative surveys, logistic SAE offers a cost-efficient instrument for directing transfers, prioritizing infrastructure, and monitoring the territorial progress of the Sustainable Development Goals. Future work should incorporate cantonal random effects, utilize real-time administrative registers, and extend the approach to multidimensional poverty to enhance policy guidance and decision-making.