<p>This paper proposes a novel Potential Energy-Guided Optimization Algorithm (PEOA), a physics-inspired metaheuristic rooted in the principle of minimum potential energy that enables natural systems to evolve toward low-energy, high-efficiency states. By emulating this principle, PEOA is designed to be an optimization algorithm that reflects this natural efficiency, delivering high robustness and excellent performance across a wide range of applications. The algorithm is rigorously evaluated against a range of classical and state-of-the-art (SOTA) methods on standard benchmark functions and real-world problems. PEOA delivers consistently top-tier performance across varied population sizes (<i>N</i> = 20, 50, 100) on standard benchmarks, as evidenced by its leading rankings, especially in high-dimensional problems where it achieves first place in approximately 80% of cases. The algorithm also maintains high-precision convergence across varying dimensions (D = 30, 50, 100), even with a small population size (<i>N</i> = 20), confirming its robustness to dimensional scaling. In real-world applications, PEOA yields the lowest objective values for engineering problems and achieves the lowest Mean Squared Error (10.7184) in neural network hyperparameter tuning in the shortest time (197&#xa0;s). These outcomes highlight both its high predictive accuracy and computational advantages, collectively establishing PEOA as a robust and efficient solver for diverse and complex optimization challenges.</p>