Metaheuristic optimization has emerged as one of the most influential computational paradigms for solving complex optimization problems characterized by high-dimensional search spaces, nonlinear objective functions, and numerous local optima. Traditional deterministic optimization techniques often exhibit limited adaptability when confronted with large-scale combinatorial and continuous optimization problems because of their dependence on gradient information and susceptibility to premature convergence. Among modern metaheuristics, Tabu Search (TS) has demonstrated remarkable capability in exploiting promising search regions through adaptive memory structures, whereas Cuckoo Search (CS) has shown superior exploration ability by utilizing Lévy flight-based random walks and brood parasitism-inspired search mechanisms. Despite their individual strengths, both algorithms possess inherent limitations that reduce optimization efficiency under complex search conditions. This study proposes a novel Cuckoo Search–Driven Tabu Search (CSDTS) approach that integrates the global exploration characteristics of Cuckoo Search with the adaptive memory-based exploitation capability of Tabu Search to establish a balanced optimization framework. The proposed approach introduces an adaptive search architecture in which Cuckoo Search dynamically generates diverse candidate solutions while Tabu Search refines these solutions using strategic memory, aspiration criteria, neighborhood evaluation, and adaptive diversification. The hybrid framework is theoretically analyzed to demonstrate its capability for avoiding premature convergence while maintaining computational efficiency. The study further examines the applicability of the proposed framework across combinatorial optimization, scheduling, graph coloring, routing, engineering optimization, and high-dimensional benchmark problems. Comparative analysis with existing Tabu Search variants and hybrid metaheuristic approaches suggests that the proposed strategy can improve convergence stability, exploration diversity, and global solution quality. The research contributes an integrated optimization framework that extends existing hybrid metaheuristic methodologies while providing practical guidance for solving increasingly complex optimization problems in engineering and computational intelligence.