Climate change, energy consumption, and public health are increasingly interconnected global issues. Rapid industrialization and increasing dependence on fossil fuel–based energy systems have resulted in rising greenhouse gas emissions and environmental degradation. These environmental changes significantly affect human health through air pollution, extreme temperatures, and ecosystem disruptions. The present research explores the statistical relationships between climate variables, energy consumption patterns, and health outcomes using quantitative statistical tools. Data from environmental and health datasets obtained from public repositories such as Kaggle were analyzed using descriptive statistics, correlation analysis, multiple regression models, time series analysis, and hypothesis testing. The findings reveal strong positive relationships between air pollution indicators and respiratory disease incidence. The analysis also shows that higher renewable energy usage is associated with reduced pollution levels and improved public health outcomes. The study demonstrates the importance of statistical modeling in understanding complex environmental-health relationships and supports the development of sustainable energy and health policies.