The recent global outbreak of COVID-19 severely impacted world health systems, human health, economies, and daily life. Countries were unprepared to tackle this emerging health crisis. Health professionals were unable to foresee the virus's spread, future developments, and the potential impact on lives should a similar pandemic occur again.The COVID-19 pandemic has profoundly disrupted global health systems, economies, and daily life, revealing significant gaps in our preparedness for such crises. This study proposes an algorithm and a set of machine learning models to predict post-pandemic health conditions, aiming to better prepare for future pandemics. Utilizing diverse datasets, including electronic health records (EHRs), public health databases, and patient surveys, In order to extract useful features from data, we preprocess and analyse it. We use a number of machine learning algorithms in our research, including logistic regression, neural networks, support vector machines, decision trees, and random forests. In order to make the model more accurate and reliable, we use strict feature selection and cross-validation procedures. Metrics like F1-score, recall, accuracy, and precision are used to assess the suggested models. We utilize SHAP values and LIME for model interpretation, ensuring transparency and understanding of the predictive factors. The findings demonstrate the potential of machine learning in forecasting long-term health conditions, thereby contributing to more robust health system responses and improved public health strategies in the event of future pandemics.