In order to achieve sustainable and effective energy management, smart grids are now essential. Improving smart grid performance in terms of energy efficiency through the regulation and control of power generation, transmission, and distribution is a major concern. The Deep Fuzzy Nets (DFN) method combines deep learning and fuzzy logic to find the most efficient way to use smart grid energy. The suggested method deduces the intricate interrelationships between smart grid system characteristics by means of a deep learning architecture. The DFN method is appropriate for practical energy management applications since the fuzzy logic part deals with data uncertainties and imprecisions. In a smart grid environment that is always changing, the suggested method can improve energy efficiency while providing accurate predictions. The suggested method using deep fuzzy nets achieved a critical success index of 96.54%, a prevalence threshold of 92.37%, a sensitivity of 91%, and a specificity of 94.55%. Several energy systems have put this method to the test, and the results show that it can increase efficiency at the system level while still letting consumers manage their own energy consumption. Energy researchers are still primarily focused on optimizing intelligent grids' energy efficiency; DFN could be a powerful answer to this problem.