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The increasing burden on emergency departments can lead to delayed patient care, increased mortality rates, and other critical consequences. Addressing this challenge requires the development of predictive models capable of anticipating fluctuations in air pollution and weather parameters and estimating the resulting emergency department ED admissions. Such models can enable healthcare systems to proactively allocate resources and ensure timely, effective patient care. In this context, our research presents a novel, AIdriven holistic predictive framework that integrates a wide array of environmental parameters derived from multiple data sources, including satellitebased observations and ground station measurements. The model accounts for lag effects—the delayed impact of environmental changes on health outcomes—to enhance the accuracy of hospital admission forecasting.This approach is particularly tailored to the Maltese healthcare context, offering a scalable and adaptable solution to support climateresilient healthcare planning.