The analysis of energy consumption serves as an integral tool for the management of energy resources within organizations, industries, and nations. Conventional techniques for energy forecasting often fail to incorporate uncertainties, including fluctuations in climate conditions or economic volatility. In this context, the Monte Carlo method emerges as a more effective strategy, as it takes into account stochastic elements and uncertainties by simulating various scenarios and estimating probability distributions for predictions. The research explores the use of various machine learning algorithms, including linear regression, decision trees, and neural networks, in predicting energy consumption based on variables such as building attributes, occupancy levels, and meteorological information. The Monte Carlo simulation is used to assess the uncertainty and risk associated with energy consumption, which enables more informed decision-making. It performs the very important role in making predictions concerning energy consumption more precise and dependable, thus helping in taking better energy resource management decisions and making planning for investments. The following dissertation outlines the basic concepts of the Monte Carlo method along with machine learning models: decision trees, linear regression, and random forests applied within energy sector. The effective administration of energy usage within structures has come forward as one of the principal goals in the pursuit of sustainability and a reduction in carbon emissions. This research will provide an elaborate review of trends in consumption patterns using a combination of Monte Carlo simulations in conjunction with machine learning methods. This framework allows for accurate forecasting of energy consumption, provides an insight into the dominant determinants influencing usage, and promotes the development of effective energy-saving strategies.