This research paper introduces a groundbreaking neutrosophic methodology tailored to confront the intricate realms of uncertainty and vagueness within the cuckoo search algorithm. The optimization problem are still have obscales like the complexity time, fitness and the uncertinaity variables. This paper presents a neutrosophic approach based on cukoo search algorithm optimizer to takle the uncertaininty and vguness problem. Moreover, the proposed algorithm deals with the three basic neutrospohic parameters which are the truth, indeterminacy, and falsity within the given variables of the applied function. We utilized a sequential hybrid approach to merge the neutrosophic with the cuckoo search algorithm in order to achieve better fitness value. The results indicated taht the fitness value reached to 1.00 which show that the best fitness value achieved using the neutrosophic cukoo search optimizer. The proposed algorithm can be utilized for problem solving and dicision making statigic in computational intelligence methodoligies.