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Optimization is central to classical and modern machine learning. This paper introduces Dynamic Fractional Generalized Deterministic Annealing (DF-GDA), a physics-inspired algorithm that boosts stability and speeds convergence across a wide range of models, especially deep networks. Unlike traditional methods such as Stochastic Gradient Descent, which may converge slowly or become trapped in local minima, DF-GDA employs an adaptive, temperature-controlled schedule that balances global exploration with precise refinement. Its dynamic fractional-parameter update selectively optimizes model components, improving computational efficiency. The method excels on high-dimensional tasks, including image classification, and also strengthens simpler classical models by reducing local-minimum risk and increasing robustness to noisy data. Extensive experiments on sixteen large, interdisciplinary datasets, including image classification, natural language processing, healthcare, and biology, show that DF-GDA consistently outperforms both state-of-the-art and traditional optimizers in convergence speed and accuracy, offering a powerful alternative for critical large-scale, complex problems across diverse scientific and industrial settings today. Check for updates