The Knapsack Problem (KP) is a classic combinatorial optimization challenge with applications in several fields including logistics and finance. Traditional solving approaches often struggle to solve large-scale and complex instances, necessitating the adoption of advanced metaheuristic techniques. This study presents a novel approach that integrates chaotic maps into the Tasmanian Devil Optimization (TDO) algorithm to address the KP more effectively. Specifically, a chaotic binarization mechanism is proposed to improve the balance between exploration and exploitation. Comparative analyses with state-of-the-art metaheuristics were performed, using KP benchmark datasets. The results demonstrate that the chaotic sine-based TDO outperforms traditional methods in terms of solution quality, convergence stability, and computational efficiency. These findings highlight the potential of chaotic hybridization as a promising tool for solving binary combinatorial optimization problems.