This study employed Principal Component Analysis (PCA) and K-means clustering to investigate the relationships between climate, energy consumption, and geographic distribution across countries. PCA revealed temperature as the primary driver of variation, with the coldest northern countries exhibiting the most significant differences from others. Subsequent K-means clustering, initially set at five groups, was optimized to four clusters following iterative analysis, enhancing the resolution of patterns. The resulting clusters delineated countries by climate and energy consumption characteristics: northern and affluent nations (e.g., the US, Canada, and Northern Europe) formed a high-energy-consumption, cold-climate group, while others separated into hotter, humid regions with varying per capita energy use. A world map visualized these groupings, highlighting their spatial coherence. The findings suggest that while climate dominates differentiation with fewer clusters, increasing cluster granularity may emphasize additional socioeconomic factors, such as GDP and energy consumption, over climatic influences. This analysis underscores the interplay of environmental and economic factors in shaping country-level energy profiles.