Energy-Based Modeling defines probabilistic systems using an energy function that assigns lower values to more likely configurations. Such models are used in machine learning for pattern recognition, density estimation, and structured prediction. By learning an energy landscape, they capture dependencies among variables without explicit normalization. Energy-based approaches have applications in vision, natural language, and physics-inspired computation. They provide a flexible framework for modeling complex data distributions.