The global healthcare systems have faced unprecedented challenges due to the COVID-19 pandemic, necessitating innovative neural computing solutions to inform critical decision-making. In this study, we introduce a neural-inspired machine learning framework to predict COVID-19 mortality risk, utilizing a dataset comprising over one million records. We developed and evaluated a suite of advanced models—Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Gradient Boost Classifier, and a neural ensemble-based Voting Classifier—to analyze the influence of demographics, symptoms, and preexisting conditions on mortality predictions. Through meticulous feature engineering and data preprocessing, our approach yielded profound insights, with the Voting Classifier achieving an exceptional 93% accuracy on test data, outperforming Random Forest and Logistic Regression (both at 91%). Key risk factors identified include age and preexisting conditions, complemented by nuanced patterns linked to symptoms and socioeconomic-demographic factors. Model robustness was rigorously validated using F1-score and ROC curves, affirming its reliability and generalization capacity. The Voting Classifier’s neural ensemble design, integrating diverse model outputs, exemplifies the power of neural computing principles in processing complex health data. This framework not only enhances predictive accuracy but also provides actionable insights for public health, enabling optimized resource allocation, prioritized care for high-risk patients, and improved survival outcomes. Beyond elucidating COVID-19 mortality dynamics, this research underscores the transformative potential of neural computing in tackling global health crises, establishing a robust foundation for data-driven strategies in future challenges.