This study provides an in-depth analysis of a global earthquake dataset, offering valuable insights into seismic activity patterns and trends worldwide. The dataset includes key parameters such as magnitude, depth, geographical coordinates, and the date and time of occurrence, collected from reliable seismic monitoring networks over several decades. The aim is to understand the spatial and temporal distribution of earthquakes, identify high-risk zones, and explore correlations between earthquake characteristics. Using statistical analysis, geospatial visualization, and data mining techniques, the research reveals that regions along tectonic plate boundaries, such as the Pacific Ring of Fire, experience significantly higher seismic activity. The distribution of magnitude and depth highlights trends that are crucial for earthquake preparedness and risk mitigation strategies. Additionally, temporal analysis suggests clustering of events in some regions, potentially linked to aftershock sequences or foreshocks. The dataset also serves as a foundation for machine learning applications, including earthquake prediction models and anomaly detection. While precise prediction remains a complex challenge, data-driven approaches offer promising avenues for future research. The study emphasizes the importance of high-quality, well-structured seismic data for scientific research, public awareness, and policymaking. Continued efforts in refining data collection and analysis are essential to enhance resilience against future earthquakes and guide infrastructure development, urban planning, and emergency response strategies worldwide.