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The quantification of actual evapotranspiration (ETₐ) is fundamental to sound water resource governance, yet current estimation methods pose significant constraints. Established methodologies, among them eddy covariance and energy balance computations reliant on remote sensing, are frequently beset by prohibitive costs, patchy spatial and temporal coverage, and deteriorating forecast fidelity, particularly for older empirical constructs. Although machine learning has emerged as a viable solution, it is not without drawbacks, especially a systematic bias toward low ETₐ predictions during high-temperature episodes. We trace this deficiency to inadequate model training on the scarce but critically significant extreme ETₐ values, paired with meteorological datasets that are limited rather than massive. Within this paper, we illustrate how few-shot, meta-learning frameworks (MAML), purpose-built to deliver superior generalization when data are constrained, can eclipse elementary machine learning formulations in the upscaling of ETₐ originating from two major in-situ observation networks, namely Ameriflux and Euroflux. Working with a sparse set of remotely sensed land surface variables sourced from the METRIC-EEFlux system alongside a narrow suite of climatic drivers, we establish that the selected models yield measurable benefits when assessed under the utility-based regression philosophy, guiding meaningful real-world decisions. Our preliminary findings show that EEflux ETₐ strays markedly from in-situ tower readings from the Ameriflux and EEflux installations (R² = 39%). By comparison, MAML attains the strongest alignment with ETₐ, outperforming both elementary machine learning architectures and EEFlux (R² = 71% on the full test corpus, R² = 0.88 under the Csa climate class, R² = 0.79 under the Cfa climate class, and R² = 0.78 for the CSH vegetation category), and steadily improves without succumbing to overfitting even when trained on a comparatively modest data pool. Its elevated F2 score (96%) indicates that MAML maintains high precision and recall for atypical instances, a feature of considerable consequence for irrigation scheduling. As a noteworthy result in its own right, this investigation corroborates that even a sparse set of remotely sensed EEflux outputs contributes materially to understanding ground-referenced ETₐ and can thus serve a valuable role in environments where the procurement of abundant, high-quality data is limited.