To the arsenal of optimization approaches inspired by nature, the proposed optimization algorithm is a more recent addition. The proposed optimization algorithm, inspired by seismic activity, simulates dynamic particle movements during earthquakes to solve complex problems. This article provides a thorough overview of its theory, methods, and applications. The review begins by outlining the algorithm’s core concepts, comparing them to earthquake dynamics, and describing its use in global optimization. It also offers an in-depth analysis of theoretical and empirical research on the algorithm’s performance and reliability, comparing it to other metaheuristics. Using CEC-2018 benchmark functions, the algorithm outperforms leading evolutionary methods across all dimensions (D = 10, 30, 50, and 100). The Wilcoxon rank-sum test shows it achieves better results than the Hybrid Sampling Evolution Strategy (HS-ES) on multiple functions across these dimensions. The paper also calculates the algorithm’s run-time complexity. Initial results highlight its feasibility, consistency, stability, and superior performance compared to contemporary algorithms.