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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 comprehensive range of environmental parameters from both satellitebased and ground station sources. By accounting for lag effects—the delayed influence of environmental changes on health outcomes—our model enhances the accuracy of hospital admission forecasting. Specifically designed for the Maltese healthcare system, this approach provides a scalable and adaptable solution to foster climateresilient and datadriven healthcare planning. Together, let us embrace innovation and work toward transforming our hospitals into intelligent, responsive systems that safeguard health in a changing world.