The N-Queens puzzle, a classical combinatorial optimization problem, continues to serve as an effective benchmark for evaluating the performance of intelligent search and optimization algorithms. Traditional deterministic methods typically struggle with scalability as the problem size increases, making metaheuristic approaches a promising alternative. This study investigates the application of metaheuristic techniques such as genetic algorithms, particle swarm optimization, simulated annealing, and ant colony optimization, with a particular emphasis on the recently emerging BRADO (Balanced Random Drift Optimization) algorithm. BRADO's adaptive drift mechanism and balanced exploration-exploitation strategy are analyzed for their suitability in navigating the highly constrained solution space of the N-Queens puzzle. Experimental evaluations compare BRADO's performance with other metaheuristics based on convergence speed, success rate, computational efficiency, and robustness across multiple board sizes. Results indicate that BRADO outperforms several conventional metaheuristic methods by achieving faster convergence and higher solution consistency, especially in large-scale N-Queens instances. The findings highlight BRADO's potential as an efficient and scalable optimization technique for complex constraint-satisfaction problems.