Power generation, urban heating, and industry all rely on natural gas, making it an extremely important component of the global energy landscape. Accurate natural gas consumption forecasting is a fundamental need in the energy sector in order to sustain a reliable supply, facilitate effective forecasting, and guarantee the continuous availability of this vital energy resource for a wide range of applications. Significant challenges exist in tracking natural gas consumption at the residential level due to excessive cost of equipment, safety risks associated with deployment, and the ineffectiveness of manual meter reading. Furthermore, the distribution of accurate services to households and gas enterprises is hampered by the absence of effective approaches for estimating household-level consumption of gas in residential regions. In this study, we present an in-depth comparison of eight machine learning techniques to enable accurate natural gas consumption predictions. The outcomes demonstrate that the Categorical Boosting (CB) model surpassed all other strategies investigated, achieving an amazing R2 score of 99.81%. The research focuses on US natural gas use from 2014 to 2024. Using two different explainability techniques, the research describes how the best-performing CB model makes predictions, offering insight into the algorithm's decision-making process. This research makes a valuable contribution to the expanding realm of knowledge concerning leveraging advanced machine learning models to optimize energy-related forecasting and decision-making processes.