<p>This paper introduces a sophisticated methodology for the optimal design of Finite Impulse Response (FIR) filters employing bio-inspired metaheuristic optimization, specifically the Chameleon Swarm Optimization (CSO) algorithm. The primary goals in FIR filter design, specifically, reducing passband ripple, increasing stopband attenuation, and meeting precise frequency response criteria, are addressed through CSO’s dynamic adaptation strategies, which markedly enhance the balance between exploration and exploitation, as well as convergence efficiency. Moreover, the applicability of CSO is broadened to encompass image denoising tasks, thus illustrating its versatility in wider signal processing applications. Benchmark evolutionary methods such as Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA) and Gray Wolf Optimizer (GWO) are compared for performance. MATLAB-based evaluations focus on measures that include algorithmic robustness, convergence behavior, and solution accuracy. According to empirical findings, CSO performs better than traditional methods in terms of optimization time and filter design quality, indicating its promise as a potent instrument in the fields of signal processing and image enhancement.</p>