The post-COVID socioeconomic landscape profoundly affects global life expectancy. Despite abundant data, pinpointing the most vulnerable remains challenging. This paper proposes a logistic regression model to address this gap. Drawing on diverse variables like World Bank life expectancy rates, undernourishment, and spending on education, health, unemployment, and sanitation, a comprehensive dataset is analyzed. Through exploratory data analysis, insights fuel the development of a prediction model. Employing multivariate logistic regression, the model's efficacy is assessed using the R-squared statistic. Enhancements involve eliminating variables with high p-values to boost prediction accuracy. Additionally, numerical data adjustments optimize model training and precision. By integrating these refinements, the model evolves into a potent tool for predicting socioeconomic impacts on life expectancy. This research underscores the importance of nuanced data analysis and model refinement in addressing complex societal challenges post-COVID, facilitating targeted interventions to safeguard human well-being.