This study explores the integration of advanced data analysis techniques to enhance earthquake risk assessment. By analyzing seismic data from multiple regions, we developed a predictive model that identifies high-risk zones based on historical earthquake patterns, geological factors, and population density. Our quantitative analysis revealed a strong correlation between specific geographic markers and earthquake frequency, leading to highly accurate seismic event predictions within defined areas. Additionally, we implemented a risk matrix to evaluate potential impacts, significantly improving resource allocation for disaster preparedness in vulnerable regions. This strategic approach enhances early warning systems, enabling proactive measures to mitigate seismic risks and strengthen community resilience. The findings underscore the critical role of data-driven methodologies in disaster management, offering a more precise and efficient framework for earthquake forecasting and risk reduction. By leveraging predictive analytics, this study contributes to advancing earthquake preparedness, ensuring that at-risk communities are better equipped to handle future seismic events through informed decision-making and targeted resource deployment.