This paper addresses the challenge of parameter estimation in nonlinear biochemical pathways, which is formulated as a nonlinear programming problem with differential-algebraic constraints. Traditional gradient-based local optimization methods often fail due to the problem's ill-conditioned and multimodal nature. The study evaluates several state-of-the-art deterministic and stochastic global optimization (GO) methods to solve this problem. A case study involving the estimation of 36 parameters in a three-step biochemical pathway is used as a benchmark. Only evolution strategies (ES) successfully solve this problem. Although stochastic methods cannot guarantee global optimality, their robustness and the known lower bound of the cost function make them the best candidates for inverse problems. The paper discusses the mathematical formulation of the inverse problem, highlighting its challenges for traditional local optimization methods. It reviews global optimization methods, distinguishing between deterministic and stochastic approaches. The study compares various GO methods, including GBLSOLVE, MCS, ICRS, DE, uES, SRES, and CMA-ES, on a benchmark problem. Results show that SRES and uES perform best, with SRES achieving the lowest cost function value (J=0.0013) after 39.42 hours of computation. The study also evaluates the performance of multistart local methods, which are found to be unsatisfactory for this type of problem. The results demonstrate that evolutionary strategies, particularly SRES and uES, are effective for parameter estimation in nonlinear biochemical pathways. These methods outperform other approaches, including simulated annealing and genetic algorithms, in terms of convergence speed and solution quality. The study highlights the importance of global optimization methods in parameter estimation, especially for complex systems with many parameters. The findings suggest that while stochastic methods require significant computational effort, they are well-suited for solving challenging inverse problems in biochemical systems. The study also emphasizes the potential of parallel computing to improve the efficiency of these methods.This paper addresses the challenge of parameter estimation in nonlinear biochemical pathways, which is formulated as a nonlinear programming problem with differential-algebraic constraints. Traditional gradient-based local optimization methods often fail due to the problem's ill-conditioned and multimodal nature. The study evaluates several state-of-the-art deterministic and stochastic global optimization (GO) methods to solve this problem. A case study involving the estimation of 36 parameters in a three-step biochemical pathway is used as a benchmark. Only evolution strategies (ES) successfully solve this problem. Although stochastic methods cannot guarantee global optimality, their robustness and the known lower bound of the cost function make them the best candidates for inverse problems. The paper discusses the mathematical formulation of the inverse problem, highlighting its challenges for traditional local optimization methods. It reviews global optimization methods, distinguishing between deterministic and stochastic approaches. The study compares various GO methods, including GBLSOLVE, MCS, ICRS, DE, uES, SRES, and CMA-ES, on a benchmark problem. Results show that SRES and uES perform best, with SRES achieving the lowest cost function value (J=0.0013) after 39.42 hours of computation. The study also evaluates the performance of multistart local methods, which are found to be unsatisfactory for this type of problem. The results demonstrate that evolutionary strategies, particularly SRES and uES, are effective for parameter estimation in nonlinear biochemical pathways. These methods outperform other approaches, including simulated annealing and genetic algorithms, in terms of convergence speed and solution quality. The study highlights the importance of global optimization methods in parameter estimation, especially for complex systems with many parameters. The findings suggest that while stochastic methods require significant computational effort, they are well-suited for solving challenging inverse problems in biochemical systems. The study also emphasizes the potential of parallel computing to improve the efficiency of these methods.