This paper presents a novel implementation of the Secretary Bird Optimization Algorithm (SBOA) for solving quadratic equations with both real and complex roots. SBOA, inspired by the Secretary Bird’s hunting strategies, efficiently explores the solution space and avoids local optima, making it more reliable than traditional methods such as the quadratic formula. The proposed algorithm iteratively refines candidate solutions using a fitness-based approach, adaptive mutation, and golden sinusoidal guiding to ensure convergence. In comparative tests, SBOA demonstrated superior accuracy, especially in handling complex roots, while maintaining efficient runtime performance. Results show that SBOA consistently outperforms conventional methods in terms of solution accuracy and computational efficiency, offering a significant contribution to evolutionary computing techniques applied to quadratic problems. This paper addresses the challenge of solving quadratic equations, which can have both real and complex roots. Traditional methods, such as the quadratic formula, may struggle with numerical accuracy and performance, especially when dealing with complex solutions.