Author Summary In many low- and middle-income countries, mothers who are overweight often live in the same household as children who are too short or too thin for their age. Nutrition programmes that try to reach such families have limited resources, so they must choose which households to prioritise. Most programmes use maternal education level as a rough filter, but whether this is actually a good way to find affected families has rarely been tested. We used surveys of 181,636 mother-child pairs from 30 low- and middle-income countries to compare three ways of identifying at-risk households: random selection, selection by low maternal education, and selection by a machine-learning model. Machine learning was much better at finding families where an overweight mother lives with an undernourished child--nearly doubling the capture rate compared with the education rule. For a different combination (underweight mother with an undernourished child), machine learning did not clearly outperform education on total recall; instead it reached different households, mostly shifting attention toward the rural poor. An unexpected finding was that the households the algorithm was most likely to miss were not the poor ones, but the wealthier and better-educated ones, where this type of malnutrition is rarer. This is not bias against the poor--it is what happens when any ranking rule operates under a fixed budget. Programmes that want to reach everyone at risk, regardless of how rare risk is in a given group, may need more than one rule.