The study of energy consumption across various building clusters offers a path to discerning intricate patterns and establishing energy efficiency metrics. However, these analyses have mostly been limited to small, controlled settings, leaving a vast potential for broader application in energy efficiency management and classification untapped. This research leverages machine learning models to determine gas consumption patterns and energy efficiency characteristics of buildings in real-life settings, based on a range of parameters including insulation properties and year of construction. The developed system was applied to a comprehensive dataset comprising eight distinct clusters of buildings, with a total of nearly 10,000 hourly gas consumption. To supplement the analysis, additional data was gathered concerning the building features. The findings indicate that the average gas consumption varies significantly across clusters, with dependencies shown for the age of the building, insulation characteristics, and building orientation The developed framework proved to be suitable for gaining insights into average gas consumption and usage patterns at a building level, non-intrusively and on a large scale. The additional data provided comparative insights between different building groups. The developed system can be easily expanded for other building characteristics and could be used to drive tailored feedback on energy efficiency improvements within buildings. This research paves the way for a more comprehensive approach to building energy efficiency, one that goes beyond the traditional parameters to include a broader set of variables such as building usage, occupant behavior,and heating system efficiency