Energy management and forecasting are becoming extremely essential all over the world due to increasing demand for energy, and hence, the more advanced system is critical. This paper proposes a that was unheard of in its ability to forecast the energy demand with the help of the meteorological data along with identifying the customer consumption pattern. The proposed technique runs various advanced prediction models including recurrent neural network (RNN) and Long Short-Term memory-Recurrent Neural Network (LTSM-RNN) to align the computation and analytics of weather change and energy consumption. By systematically integrating the various factors associated with influencing energy needs in this way, our aim is to achieve an overall understanding of the different aspects that can be effectively used to make informed decisions for optimal energy consumption and efficient usage. Thus, we explore other machine learning methods, boosting, and averaging schemes to combine different regression techniques in an effort to achieve the best result. The outcomes indicate that deep neural network models ARE substantially better than conventional time-series models, and thus, that integrating climate parameters into load forecasting models is essential. Namely, broader models that imply averaging strategies are more effective than models applying boosting strategies for the majority of the months. In sum, our conclusions indicated that climate plays significant roles in making ensemble modeling as the most accurate forecasting tool. Apart from enhancing knowledge in estimations of energy consumption, this progressive method has immense potency to impact policies and societies and to propel the shift to a sustainable energy environs.