The recent advancements in the field of predictive modeling and energy forecasting have shown a significant shift towards the integration of deep learning techniques with traditional methods. Researchers are increasingly leveraging hybrid models that combine the strengths of various neural network architectures, such as Transformers, LSTMs, and CNNs, to enhance the accuracy and reliability of forecasts. These models are being applied across diverse sectors, including weather prediction, building energy consumption, renewable energy demand, and gasoline consumption forecasting. Notably, the use of advanced deep learning models has demonstrated substantial improvements in prediction accuracy, often outperforming traditional numerical weather prediction models and classical forecasting methods. The focus on interpretability and the development of novel statistical methodologies to understand the factors driving predictions are also gaining traction. These innovations are not only advancing the field but also providing valuable tools for policymakers and industry professionals to make informed decisions. The integration of environmental decision support systems with deep learning models is particularly noteworthy, as it addresses the growing need for sustainable energy management and carbon emission reduction. Overall, the field is moving towards more sophisticated, data-driven approaches that promise to revolutionize how we predict and manage energy resources in the face of global climate change.