In the context of future pandemic preparedness, strategic vaccination prioritization emerges as a critical facet of effective response strategies. The allocation of vaccines during pandemics is a crucial component of public health response, requiring strategies that can dynamically adapt to evolving situations and ensure equitable distribution. Drawing from the recent experience of the COVID-19 pandemic, the study employed Machine Learning (ML) techniques to develop a vaccination prioritization model to improve response to emerging pandemics. To train and validate the model, a dataset comprising medical records of 3800 confirmed COVID-19 patients was sourced from the Kaggle repository. These health records were utilized to train, test, and validate six Machine Learning (ML) models. The performance of these models was compared using metrics such as precision, sensitivity, accuracy, and area under the curve (AUC) scores. The performance analysis of the four models on the datasets revealed that LightGBM emerged as the top performer, with XGBoost and Random Forest closely trailing behind. Consequently, the LightGBM model was selected for further development. Its potential adoption by health authorities and partners could significantly enhance decision-making processes regarding vaccine administration. The findings underscore the potential of machine learning to enhance decision-making processes, offering a robust framework for policymakers and health organizations to implement more efficient and equitable vaccination campaigns. This research highlights the transformative role of machine learning in public health preparedness, paving the way for smarter, data-driven responses in future pandemics.