The COVID-19 pandemic has underscored the critical challenges faced by healthcare systems worldwide, particularly in meeting the escalating demand for resources such as ICU beds, specialized care, and medical equipment. This shortfall has resulted in significant loss of life, highlighting the urgent need for accurate and timely diagnosis to optimize patient outcomes and reduce healthcare costs. In response to these challenges, our research focuses on developing a machine learning system capable of predicting whether patients will require ICU admission or can be managed remotely at home during peak periods of demand. Leveraging a novel two-dimensional reduction approach that combines evolutionary algorithms, Pattern Discovery, and machine learning techniques, we aim to streamline patient-collected data to train predictive models capable of forecasting ICU needs and remote care requirements. By providing healthcare systems with the ability to anticipate patient needs during critical phases of the pandemic, our predictive model empowers healthcare providers to allocate resources more effectively, optimize patient care delivery, and mitigate the impact of healthcare crises. The results of our experimental evaluation demonstrate the promising potential of our approach in addressing the pressing challenges posed by the COVID-19 pandemic and similar public health emergencies.