The ongoing global healthcare challenges underscore the need for accurate and efficient predictive tools capable of assessing disease severity. This study introduces a broadly applicable artificial intelligence (AI) framework designed to enhance disease severity predictions, focusing on analyzing feature importance and optimizing hyperparameters using the Hyperband method. Utilizing a dataset of 1215 patients, we employed L1 regularization to pinpoint a minimal yet highly informative set of biomarkers-oxygen saturation (O2SAT), partial pressure of carbon dioxide (PCO2), age, percentage of lymphocytes, and percentage of neutrophils. These biomarkers were found sufficient to predict disease severity with 95% accuracy. The Hyperband method facilitated efficient and effective tuning of our neural network models, enhancing their predictive capabilities. Although initially developed for COVID-19, our streamlined model can be adapted to other diseases with similar data characteristics, thereby improving predictive efficiency and impacting healthcare resource allocation and patient management during pandemics. Our findings demonstrate the potential of targeted AI applications to not only refine response strategies during health crises but also provide insights that could lead to more informed and effective healthcare practices globally.