Health surveys often sample clusters within strata and then individuals within clusters, and flexible outcome and treatment models complicate inference for adjusted contrasts from such surveys. We establish finite-population limit theory for survey-weighted augmented inverse-probability-weighted estimation within the double machine learning framework, using cross-fitting that keeps whole clusters together; a cross-fitted targeted maximum likelihood estimator inherits the results under additional targeting conditions. With first-stage clusters drawn by simple random sampling without replacement within strata, the theory allows arbitrary within-cluster dependence, appreciable sampling fractions, and many small strata, a few large strata, or both. Under bounded cluster sizes, a cluster-generation model and nuisance convergence-rate conditions, the estimator is asymptotically normal about the finite-population contrast, and a stability property derived from the cluster model controls the cross-fitting error without entropy conditions. The stratified ultimate-cluster variance estimator, without a first-stage finite-population correction, consistently estimates a with-replacement variance that is at least the without-replacement design variance of the normal limit, so the resulting intervals are asymptotically conservative, and exact when the excess is negligible. In simulations, near-nominal or higher coverage coexisted, in different settings, with remainder diagnostics above the study's negligibility benchmark and with inaccurate standard errors. In three Service Provision Assessment surveys from Malawi and Haiti, each frame treated as a fixed population and resampled 500 times, mean reported standard errors were 4%-20% larger than the empirical standard deviations, while coverage of whole-frame reference values was near or above nominal.

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