This paper presents a Chaos–Infused Moth–Flame Optimizer (CI-MFO). The parent algorithm is modified to account for deviations in search agent (moth) flight trajectory and variations in the flame orientation for an enhanced balance between exploration and exploitation tendencies. Actual photographic evidence showing light traces of such phototactic moths in-flight has been used to model flight path deviations using Chaos Theory. This approach considers their intelligence and erratic flight behavior (when subjected to excessive crowding). The performance of the developed CI-MFO algorithm is investigated comprehensively using a suite of fifty-eight benchmarking functions, including seven unimodal, six multimodal, ten fixed-dimension multimodal, six CEC-2005 hybrid-composite, and twenty-nine CEC-2017 hybrid-composite functions. The proposed algorithm's effectiveness is tested against several classical algorithms and some modified metaheuristic optimization algorithms in terms of obtained mean optima and standard deviations, and scalability analysis is also performed. The paper concludes by solving several real-world problems and comparing the proposed algorithm’s performance against several reported algorithms. The proposed algorithm exhibited a substantially better solution-finding ability.