The Snake Skin Shedding Optimization (SSSO) is a novel nature-inspired optimization technique designed to enhance global search efficiency and local exploitation through dynamic movement strategies and adaptive shedding mechanisms. Inspired by snake locomotion patterns, SSSO integrates serpentine, concertina, sidewinding, and rectilinear movements to balance exploration and exploitation. Additionally, the shedding mechanism replaces poorly performing solutions with elite-guided renewals, ensuring population diversity and preventing premature convergence. The algorithm’s performance is rigorously tested on a set of standard benchmark functions, including Sphere, Rosenbrock, Rastrigin, Griewank, Ackley, Levy, and Schwefel functions. Comparative analysis with well-established optimization algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Cuckoo Search (CS) demonstrates that SSSO achieves superior results in terms of convergence rate, solution accuracy, and stability. Tested on 7 benchmark functions, SSSO outperforms classical algorithms like PSO and ABC, achieving 1.15 × 10⁻10 on Levy and −10.07 mean on Schwefel, with low standard deviation. The algorithm demonstrates robust performance and superior convergence behavior across varied landscapes. Statistical analysis highlights its robustness, with low standard deviation values indicating consistent performance across multiple runs. These findings suggest that SSSO is a highly effective and adaptable optimization approach, making it a promising candidate for solving complex real-world optimization problems.